potential effects of information and communication technology (ict) and social...
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Doctoral Dissertation of Transport Studies Unit, Tokyo Institute of Technology, TSU-DC2011-001
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POTENTIAL EFFECTS OF INFORMATION AND COMMUNICATION TECHNOLOGY (ICT) AND SOCIAL DIMENSION ON TRAVEL BEHAVIOR GRACE UAYAN PADAYHAG DOCTORAL DISSERTATION TSU-DC2011-001
Transport Studies Unit, Tokyo Institute of Technology
March 2011
POTENTIAL EFFECTS OF INFORMATION AND COMMUNICATION TECHNOLOGY (ICT) AND SOCIAL
DIMENSION ON TRAVEL BEHAVIOR
（情報通信技術と社会的次元が交通行動に及ぼす潜在的影響）
by
GRACE UAYAN PADAYHAG
B.S. Civil Engineering, Xavier University – Ateneo de Cagayan, Philippines (1999) M.S. Civil Engineering, University of the Philippines, Philippines (2002)
Submitted to the Department of Civil Engineering, Graduate School of Science and Engineering,
in partial fulfillment of the requirement for the degree of
DOCTOR OF ENGINEERING
at
Tokyo Institute of Technology Tokyo, Japan
Dissertation Committee:
Associate Professor Daisuke Fukuda (Supervisor) Professor Osamu Kusakabe Professor Tetsuo Yai Associate Professor Yasunori Muromachi Associate Professor Shinya Hanaoka Associate Professor Jan-Dirk Schmöcker
September 2010
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ABSTRACT
Information and Communication Technology (ICT) has the potential to induce or reduce
physical travel. And recently, the impact of social dimension on activity-travel has just
gained considerable attention in the realm of transportation planning. This study explores the
potential effects of both information and communication technology and social dimension on
travel behavior.
The effects of mobile phone and telecommuting as ICT are analyzed to investigate
Londoner’s travel behavior by focusing mainly on trip frequency, number of tours and tour
complexity. Mobile phones, nowadays, are ubiquitous communication tools that can be used
to reduce a person’s travel needs or induce new travel demand. Likewise, working with a
personal computer from home might reduce trips to the office but several studies have also
suggested that it might increase other types of trips. The data used in this study is taken from
the London Area Travel Survey 2001, providing us with a large sample size of 87,148 trips.
The results of our descriptive and multivariate regression analysis imply that mobile phone
possession significantly and positively affects total trips made though not necessarily tour
complexity. The study provides good evidence that mobile phone possession is clearly
associated to total tours made. Though telecommuting does decrease work trips, other trips
like shopping or leisure trips are likely to increase. We provide further evidence that it is the
simple home-work-home tours which decrease through telecommuting and which are
replaced by other tour types, keeping the total tour numbers fairly constant. Controlling for
geographic characteristics, we further find that population density has an effect on leisure
trips and tour complexity but not on the number of work or shopping trips.
The effects of social dimension such as social interaction, social activities, social network and
time planning on travel behavior are examined through the case study in Metro Manila,
Philippines. Initially, the effects of socialization on travel are investigated with focus on
university students’ activity-travel behavior as influenced by the level and form of their
socializing practices. It is hypothesized that socialization would greatly affect the number of
side-trips students took while returning home after class. Data were collected at pre-selected
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universities in Metro Manila. Path analysis results suggested that certain types of
socialization had significant effects on the frequency of participants’ side-trip as they were
returning home. Furthermore, social network size had a significant effect on patterns of after-
class activity travel. The results may imply that socialization provides sound motivation for
trip generation and might be prospect for consideration in the future development of
transportation planning processes especially in the developing countries.
In addition, the effects of social dimension on travel are examined in the context of university
workers in Metro Manila. Social dimension includes social interaction, social activities and
social network. Structural equation model analysis was used to analyze the causal
relationship between social dimension and travel. The estimation results indicate that there
exists a positive and significant effect of social interaction on social network and social
activities. Social interaction also has an indirect positive and significant effect to social
activities via social network. In addition, social activities portray a strong and significant
positive effect on the degree of travel. These findings imply that social factors play an
essential role in the study of travel behavior in developing countries. Similarly, the effects of
ICT use on time planning, social activity participation, social network on travel behavior are
further investigated. The result indicates that ICT use may have a direct effect on time
planning, social network, social activity participation and only indirect effect on travel
behavior. Travel behavior may be related and directly affected by social network, social
activity participation and time planning. Based on these empirical results, the implications to
transport policy analysis is that shorter time planning might tend to increase social activity
participation, which may also entail new possibilities of travel patterns. The main reason for
this is because of ICT use tends to loosen time and spatial constraints.
It was found that both ICT and social dimensions reveal significant effect on individual travel
behavior and activity participations.
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DEDICATION
This d issertation is d edicated to m y f amily e specially to m y f ather E ddie f or th e unconditional love you always bestowed on me. To my dearest mom Ging and to my loving siblings, Mae, Dixie, Charles and Richmond for all the prayers and for always believing in me that I can do it no matter how hard the task can be.
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ACKNOWLEDGMENT
The process of doing a dissertation can be tough and laborious but working with competent
people it becomes light and manageable to handle. These people needs special mention for
this dissertation would have not been possible without the immense help from them.
First and foremost, I would like to express my greatest gratitude to my adviser, Dr. Daisuke
Fukuda, for continuously providing the academic guidance ever since I started my research in
the lab. Most importantly, I heartily thank you for providing me the timely and instructive
comments and evaluation at every stage of the thesis process, granting me to finish this
project on schedule.
I would like to express my profound gratitude to Dr. Jan-Dirk Schmoecker, for furnishing me
the London data and for the constant sensible advice and guidance.
I genuinely thank all Transportation Planning professors: Prof. Tetsuo Yai, Assoc. Prof.
Yasunori Muromachi, and Assoc. Prof. Shinya Hanaoka for the constructive insights you all
have given during the regular seminars and summer seminars that guided and challenged my
critical thinking, substantially improving the finished product. Also, I owe a big thank you to
all Civil Engineering professors especially Prof. Osamu Kusakabe for imparting valuable
comments making this thesis sound and more meaningful.
Special thanks go to all my laboratory mates for sharing the invaluable assistance as well as
to my professors back in the Philippines for the continuous support when I did my survey.
Likewise, I thank all my friends who have extended their arms and share their precious time
to help with the data collection process as well as for the social support they offered.
The author would like to convey gratitude to the Ministry of Education, Culture, Sports,
Science and Technology (MEXT) for generously providing the financial means to study in
Japan and to Japan Society for the Promotion of Science (JSPS) for funding the survey in
Metro Manila.
To God almighty, thank you for all the blessings you have bestowed on me especially for
giving me these amazing people to help lessen the burden throughout the thesis course.
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TABLE OF CONTENTS
Chapter title .......................................................................................................................... Page
ABSTRACT ................................................................................................................................. i
DEDICATION .......................................................................................................................... iii
ACKNOWLEDGMENT............................................................................................................ iv
LIST OF FIGURES ................................................................................................................... ix
LIST OF TABLES ................................................................................................................... xii
CHAPTER 1 Introduction......................................................................................................... 1
1.1 Research background ............................................................................................... 1
1.1.1 The emergence of ICT ............................................................................................ 4
1.1.2 Fundamentals of social dimension ........................................................................ 12
1.2 Research motivation ............................................................................................... 16
1.2.1 The prospective role of ICT in transportation research ........................................ 18
1.2.2 The vital role of ICT in developing countries ....................................................... 18
1.2.3 Social dimension in transportation studies............................................................ 23
1.3 Research objectives and scope ............................................................................... 24
1.3.1 Scope of research .................................................................................................. 25
1.3.2 Significance of research ........................................................................................ 26
1.4 Definition of terms ................................................................................................. 27
1.5 Organization of the thesis ....................................................................................... 29
CHAPTER 2 Literature Review ............................................................................................. 32
2.1 Introduction ............................................................................................................ 32
2.2 ICT phenomenon and travel tendencies ................................................................. 33
2.2.1 Substitution Effect................................................................................................. 33
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2.2.2 Complementary effect ........................................................................................... 36
2.3 ICT and effects on time planning ........................................................................... 38
2.4 Sociological approaches to analyze travel .............................................................. 39
2.4.1 Social activity participation ..................................................................................... 39
2.4.2 Social network structure .......................................................................................... 43
2.4.3 Theoretical models of social interactions ................................................................ 53
2.5 Social dimension and ICT ...................................................................................... 54
2.6 Summary and discussion ........................................................................................ 55
CHAPTER 3 Information and Communications Technology Adoption and Trips ............... 57
3.1 Introduction ............................................................................................................ 57
3.2 Literature review .................................................................................................... 58
3.2.1 Previous studies..................................................................................................... 58
3.2.2 Hypotheses ............................................................................................................ 60
3.3 Data structure and descriptive Analysis ................................................................. 62
3.3.1 Overview of London ............................................................................................. 62
3.3.2 Data description .................................................................................................... 65
3.3.3 Descriptive analysis of mobile phone impact ....................................................... 66
3.3.4 Descriptive analysis of the impact of using home PC for work ............................ 70
3.4 Regression analysis ................................................................................................ 73
3.4.1 Model specification ............................................................................................73
3.4.2 Control variables in regression model ................................................................75
3.4.3 Effects on trips per day .......................................................................................79
3.5 Summary and discussion ........................................................................................ 82
CHAPTER 4 Information and Communications Technology Adoption and Tour
Complexity ................................................................................................................................ 84
4.1 Introduction ............................................................................................................ 84
4.2 Literature review .................................................................................................... 85
4.2.1 Relevant studies .................................................................................................... 85
4.2.2 Hypotheses ............................................................................................................ 86
4.3 Data Structure and descriptive analysis .................................................................. 88
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4.3.1 Data description .................................................................................................... 88
4.3.2 Descriptive analysis of mobile phone impact ....................................................... 89
4.3.3 Descriptive analysis of the impact of using home PC for work ............................ 90
4.4 Regression analysis ................................................................................................ 90
4.4.1 Model structure .....................................................................................................90
4.4.2 Effects on number of different tour types made ...................................................92
4.4.3 Effects on tour complexity ....................................................................................97
4.5 Summary and discussion ...................................................................................... 101
CHAPTER 5 The Effects of Social Interaction and Social Network on Travel Behavior ... 103
5.1 Introduction .......................................................................................................... 103
5.2 General hypothesis ............................................................................................... 108
5.3 Overview of the study area ................................................................................... 111
5.3.1 Overview of Metro Manila ................................................................................. 111
5.4 Survey method ...................................................................................................... 114
5.4.1 The sample .......................................................................................................... 115
5.4.2 The questionnaire ................................................................................................ 116
5.4.2.1 The main body of the questionnaire ............................................... 116
5.4.2.2 The name generator ........................................................................ 118
5.5 Empirical analysis ................................................................................................ 120
5.5.1 Structure of the empirical path model ..............................................................120
5.6 Results and discussion .......................................................................................... 125
5.7 Synthesis ............................................................................................................... 127
CHAPTER 6 The Effects of Social Activity to Travel Behavior as an intermediate factor of
Travel Behavior ...................................................................................................................... 128
6.1 Introduction .......................................................................................................... 128
6.2 Model of social factors and travel ........................................................................ 131
6.3 Survey method ...................................................................................................... 133
6.3.1 Questionnaire development ................................................................................. 133
6.3.1.1The primary questionnaire ................................................................ 133
6.3.1.2The ego-centered network................................................................. 134
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6.3.2 Data ..................................................................................................................... 135
6.4 Empirical analysis ................................................................................................ 140
6.4.1 Structural equation model analysis ...................................................................140
6.4.2 Model specification ..........................................................................................141
6.5 Model estimation results and discussion .............................................................. 144
6.6 Synthesis ............................................................................................................... 148
CHAPTER 7 The Effects of ICT Use on Time Planning and Social Dimensions to Travel
Behavior .................................................................................................................................. 150
7.1 Introduction .......................................................................................................... 150
7.2 Hypotheses ........................................................................................................... 153
7.3 Data and analysis .................................................................................................. 156
7.4 Results and discussions ........................................................................................ 160
7.5 Synthesis ............................................................................................................... 165
CHAPTER 8 Conclusion and Future Recommendations ...................................................... 167
8.1 Summary and conclusions .................................................................................... 167
8.2 Potential applications of the study ........................................................................ 172
8.3 Further studies for recommendation ..................................................................... 174
REFERENCES ....................................................................................................................... 176
APPENDICES ........................................................................................................................ 188
Appendix 1 (Alternative structural models for Chapter 6) ............................................... 189
Appendix 2 (Alternative structural models for Chapter 7) ................................................ 192
Appendix 3 (Sample of the survey questionnaire of London Area Travel Survey 2001 Household Survey Project Report) ..................................................................................... 195
Appendix 4 (Sample of Survey documents in 2007 for university students in Metro Manila, Philippines: Survey cover letter and survey questionnaire) ............................................... 206
Appendix 5 (Sample of Survey documents in 2008 for university workers in Metro Manila, Philippines: Survey cover letter and survey questionnaire) ............................................... 218
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LIST OF FIGURES
Figure 1.1 The general conceptual framework of the study ...................................................... 2
Figure 1.2 ICT penetration around the world, by regions, 2007 Source: ITU World
Telecommunication, 2008 ................................................................................................ 6
Figure 1.3 ICT penetration by country (in the developing and developed countries) Source:
ITU World Telecommunication, 2008 ............................................................................. 7
Figure 1.4 Illustrates the statistical data for SMS: a.) Shows the SMS revenue in 2007. b.)
Presents the top texters in 2007 Source: http://www.spectrum.ieee.org/oct08/6817 ...... 8
Figure 1.5 Face-to-face and telephone sociability orientation (family versus friends) through
life cycle Adapted from Smoreda et al. (2001) ................................................................ 9
Figure 1.6 Activity type Adapted from Silvis and Niemeier (2006) ....................................... 12
Figure 1.7 Participation in social activity groups, by types of organizations, 2003 Source:
Statistics Canada. 2003 General Social Survey on Social Engagement ......................... 14
Figure 1.8 Fixed telephone subscribers Source: ITU World Telecommunication, 2008 ........ 20
Figure 1.9 ICT subscribers per 100 inhabitants: Fixed telephone subscribers Source: ITU
World Telecommunication, 2008 ................................................................................... 20
Figure 1.10 ICT subscribers per 100 inhabitants: Mobile phone subscribers Source: ITU
World Telecommunication, 2008 ................................................................................... 21
Figure 1.11 ICT subscribers per 100 inhabitants: Internet users subscribers Source: ITU
World Telecommunication, 2008 ................................................................................... 21
Figure 1.12 Internet subscription between UK and the Philippines ........................................ 22
Figure 1.13 Landline phone subscription between UK and the Philippines ............................ 22
Figure 1.14 Structure of Research ........................................................................................... 30
Figure 2.1. Relative substitution among communications modes, simultaneous with absolute
expansion of all modes Adapted from Mokhtarian (1990) ........................................... 35
Figure 2.2 General model structure of travel behavior Adapted from Lu and Pas (1999) ...... 41
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Figure 2.3 Information technology profiles of social network (Adapted from Smoreda and
Thomas, 2001) ................................................................................................................ 45
Figure 2.4 Media and timing of announcements by relational proximity of the correspondent
Adapted Licoppe and Smoreda (2005) ........................................................................... 47
Figure 2.5 Schematic of the iterative recruitment method Adapted from, Silvis and Niemer
(2006) ............................................................................................................................. 48
Figure 2.6 Example of a friendship network: the knows everyone case Adapted from
Brueckner (2006) ............................................................................................................ 51
Figure 2.7 Geographical layout of the model with social network centered on an ego Adapted
from Hackney and Axhausen (2006) .............................................................................. 52
Figure 3.1 Illustration of hypotheses (a) shows the hypothesis of the effect of mobile phone
possession on trips as stated in A.1 (b) represents the hypothesis of the effect of
telecommuting on trips as discussed in A.2, A.3, A.4.................................................... 61
Figure 3.2 Population densities in London (1996-2008) ......................................................... 62
Figure 3.3 Working population by gender in London (year 2007) .......................................... 63
Figure 3.4 Mobile phone, Landline, Internet subscription ....................................................... 64
Figure 3.5 Effects of mobile phone possession on trip frequency (for each type of trip) ....... 70
Figure 3.6 Average number of trips and the duration of personal computer use to work from
home ............................................................................................................................... 71
Figure 4.1 Illustration of hypotheses (a) shows the hypothesis of the effect of mobile phone
possession on tour number and tour complexity as stated in A.1 and B.1 (b) represents
the hypothesis of the effect of telecommuting on tour numbers and tour complexity as
discussed in A.2 and B.2 ................................................................................................ 87
Figure 4.2 Types of simple tours ............................................................................................. 93
Figure 4.3 Types of complex tours .......................................................................................... 94
Figure 5.1 Mobile cellular subscribers as a percent of total telephone subscribers, selected
countries, 1996 ............................................................................................................. 105
Figure 5.2 Number of trips according to age group of the respondents ............................... 107
Figure 5.3 Proposed exploratory factors influencing after-class side-trips ........................... 109
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Figure 5.4 Map of the study area ........................................................................................... 111
Figure 5.5 Population densities in Metro Manila (1996-2008).............................................. 112
Figure 5.6 Working population by gender in Metro Manila (year 2007) Source:
http://www.bles.dole.gov.ph (2007) ............................................................................. 113
Figure 5.7 Mobile phone, landline, internet subscription and per capita in the Philippines
Source: ITU, 2008 ........................................................................................................ 113
Figure 5.8 Sample of name generator used in the survey ...................................................... 119
Figure 5.9 Estimated causal relationship model of socialization and number of side-trips
taken on the way home ................................................................................................. 121
Figure 6.1 Schematic Image of Social network ..................................................................... 130
Figure 6.2 Conceptual model of the study ............................................................................. 131
Figure 6.3 The estimation results of the structural equation modeling .................................. 145
Figure 7.1 Adults’ time use over two centuries (Adapted from Gershuny, 2000)................. 152
Figure 7.2 Hypothesis of the effects of ICT use .................................................................... 154
Figure 7.3 The estimation result of the structural equation ................................................... 161
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LIST OF TABLES
Table 1.1 Daily travel: Distribution of trips by trip purpose ................................................... 13
Table 3.1 Household car ownership by area of residence ....................................................... 65
Table 3.2 Mobile phone and personal computer information .................................................. 67
Table 3.3 Mobile phone penetration rate by agencies ............................................................. 67
Table 3.4 Mobile phone penetration rate by Age .................................................................... 68
Table 3.5 Mobile phone penetration rate by Income ............................................................... 68
Table 3.6 Penetration rate by Employment type (LATS 2001 Sample) .................................. 68
Table 3.7 Average number of trips per day by destination and by work type ......................... 72
Table 3.8 Ordered probit models for number of weekday trips ............................................... 76
Table 3.9 Average household income (in £) by telecommuting status .................................... 81
Table 4.1 Mobile phone and personal computer possession.................................................... 89
Table 4.2 Effects of mobile phone possession on the average number of tours for each tour
type ................................................................................................................................. 96
Table 4.3 Effects of work type and telecommuting status on the average number of tours for
each tour type ................................................................................................................. 96
Table 4.4 Ordered probit model on tour complexity ............................................................... 98
Table 5.1 Number of Mobile phone subscribers in the Philippines from 1996-2005........... 106
Table 5.2 Household car ownership in Metro Manila ........................................................... 114
Table 5.3 Descriptive statistics of the respondents (N=287) ................................................. 116
Table 5.4 Descriptive statistics of the variables used for path analysis (N=287) .................. 122
Table 6.1 Descriptive statistics of the university worker participants in Metro Manila (235)
...................................................................................................................................... 136
Table 6.2 Frequency of information and communication technology (ICT) Use.................. 139
Table 6.3 Social network descriptive dimension ................................................................... 139
Table 6.4 Latent variables and observed variables used in the model ................................... 142
Table 7.1 Categories and average number of friends ............................................................ 157
Table 7.2 Descriptive result of university workers and students with N = 522 ..................... 157
Table 7.3 Latent and observed variables used in the analysis ............................................... 159
Table 7.4 Measurement variables, standardized parameter estimates and the t-values ......... 162
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CHAPTER 1 INTRODUCTION
1.1 Research background
During 20th century, a rapid proliferation of information and communication technology
(ICT) such as mobile phones, electronic mail (e-mail), internet and even e-commerce has
become widespread. This has also changed the lifestyle of many people in different ways.
For example, in the transportation aspect, some trips are induced and some are reduced or
substituted due to ICT. Applications of ICT that might enable to induce or increase travel are
mobile phones, computers and fax machines while those that might to substitute or eliminate
travel are applications like teleconferencing, telecommuting and online banking.
The diffusion of ICT differs between developed and developing countries. The developed
countries adopt ICT quickly and widely. This could be one of the reasons with regard to
studies of ICT-related travel that mostly emerged from the developed countries. And based
from the experience of ICT in the developed countries, the developing countries can acquire
and learn lessons in formulating transportation policies. For example, if ICT is found to
reduce physical travel then it helps alleviate traffic congestion subsequently air pollution is
reduced.
Apart from travel, ICT also has effects on social dimension, which composed of social
activities, social interaction and social network . For example, social interaction nowadays
can be done through different ICT applications like an interaction through mobile phone
from/to family and friends or the constant sending of emails as a form of communication for
work or personal matters. These ICT applications are also used to coordinate or to organize
social activities. For instance, a phone call is made to invite friends to go on a barbeque party
or an email is sent to everyone in the workplace for the planned team building activities. In
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social activities, people will be able to meet and gain more friends, nurture old-established
relationships and even create new ones thus creating one’s social network.
Social dimension affects travel in various yet in obscure ways. For example, if a person has
large group of friends then the possibilities of making travel is high. And if ever a person
interact with his/her friends or family members more often will likely engage to make travel.
Moreover, if a person participates frequently in social activities then is more likely to make
trips.
Thus, the general conceptual framework arises for this study, as shown in Figure 1.1. It
illustrates that ICT, social dimension and travel might have interrelationship in somehow
complex ways.
Figure 1.1 The general conceptual framework of the study
ICT use
Social dimension Travel behavior
?
e.g. Mobile phoneInternet connectionOnline chat
e.g. Social interactionSocial networkSocial activities
e.g. Mode choiceTrip frequencyTrip destination
?
?
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The social dimension is potentially or by nature often attached in travel purposes yet found to
be intangible. A person’s travel may be 1) motivated by social intentions 2) due to the innate
cognitive nature of a person as being social, or nowadays 3) propelled by the availability of
communication tools that enhance sociability (e.g. mobile phones, email or chat). According
to Mokhtarian and Salomon (2002), the empirical research that pertains to the impacts of
telecommunications technology on travel is classified into three categories. First is the
macro-scale category that includes studies of the entire sectors of the economy at regional or
higher levels. The second category focuses on a particular application, e.g., telecommuting or
teleconferencing. The last category broadens to include all or most communication and travel
activities. The present study slightly touches and deals the aspect of the second and third
categories, which focuses on telecommuting and travel/social activities, respectively.
In the past few decades, the main determinants of travel behavior are regarded as socio-
demographic characteristics, socio-economic motives and some person-specific
psychological aspects. A couple of years ago, the development of analyzing travel behavior
arrive at examining the activity participation which has been immensely scrutinized its effects
on travel (for example, van der Hoorn, 1979; Axhausen and Gärling, 1992; Golob and
McNally, 1997; Ettema and Timmermans, 1997; Bowman, 1998; Lu and Pas, 1999; Kuppam
and Pendyala, 2001; and Timmermans, 2005). In spite of inexhaustible studies and analysis
between activity patterns and travel popping around for years, there are still studies that
continuously in search for a better understanding of travel behavior patterns. Some have
modified and augmented travel behavior models by testing several possible factors that might
play a significant role in analyzing travel behavior. Later on, McNally (2008) supported the
idea when he pointed out that travel is almost always viewed in theory as derived from the
demand for activity participation. And yet, in practice it has been modeled mostly with trip-
based, specifically, the well-known four (4) step travel model rather than activity-based
methods in the practice of travel demand forecasting.. The four step model (i.e., trip
generation, trip distribution, mode choice, trip assignment) is also known as FSM and it is
used as a tool to forecast the future travel demand. The basic structure of the model is
sophisticated by Manheim (1979) and was later expanded by Florian et al. (1988) by
including the activity location procedure in the entire conceptual structure of FSM.
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Just in time when ICT is at its peak when travel behavior analysis tackles issues on social
dimension. Especially, technologies nowadays serve as tools for planning and coordination
of activity more so for interaction, which eventually leads to making people travel more.
1.1.1 The emergence of ICT
Looking back to the early years of communication technology, when Alexander Graham Bell
first discovered the telephone, he called his friend on the telephone and his first words were
“Mr. Watson, come here, I want to see you” (Watson, 1974). Those first words he uttered on
the telephone are an indication that communication and technologies have implications on
travel. However, telephone was originally conceived and marketed as a business tool and was
fully recognized as an essential component of the business; it took several decades before its
social use for residential customers (Fischer, 1992). Even the early plans for networked
computers did not foresee their social potential, but early users of the system developed email
just within two years of the first connection. The web was initially conceptualized as an
academic publishing tool, yet personal homepages with photos, web journals, and web links
to friends appeared almost the instant an accessible web browser is available. These days,
there is an increasing awareness of the significance of the social uses of media and much
more attempt is being prepared to create cautiously sociable media.
In previous years, in order to participate in a social activity, time and location is strictly set
because once a person misses it then the planned social activity will be ruined. Nowadays,
information and communications technology (ICT) makes the organization of social activity
more flexible and in coordination. For example, if you are going to a meet up a person at a
train station you have to decide the exact time to meet up and be there exactly on time or
even ahead of time; if not, you will suffer the consequences of not meeting him at all.
However, if a person has mobile phone, it enables him to make a call that he will be late for a
few minutes then it prevents him from worrying on time constraints for being late. Other
example is that when a person has a mobile phone he can make an immediate call to re-
schedule a meeting due to some urgent errand.
ICT is an umbrella term that includes any communication device or application,
encompassing: radio, television, cellular phones, computer and network hardware and
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software, satellite systems and soon, as well as the various services and applications
associated with them, such as video-conferencing and distance learning (SearchCIO-
Midmarket.com, 2008). Formerly, communication technologies are not necessarily designed
for sociability (Bainbridge, 2004). They are often produced within the context of engineering
and business fields that provide efficiency and utility. In fact, the history of communication
technologies emanates from both the underestimation of the importance of social
communication and the eagerness of people at adapting media for social purposes.
Nevertheless, people, as previously mentioned, being a social being instantly realize the
social uses for any communication mode (e.g, mobile phone or internet). Hence, at this age
of high technology, social dimension might be more pronounced nowadays due to the
functions of ICT being quick and most of the time efficient.
If it ever happen that communication infrastructure or, should we say, ICT development is
well-established, subsequently more movements and exchanges are maintained. This is
because, according to Mokhtarian (1990), ICT permits a great deal of flexibility whether,
when, where, and how to travel. Thus, ICT enables to loosen constraints due to time structure,
home or work location. It allows people to take advantage of the excess capacity in the
transportation system at off-peak times and places that promotes more efficient use of
existing capacity and delays the need to construct expensive new infrastructure will not be a
necessity.
Remarkably, the growth of the mobile sector has been able to change the ICT landscape more
rapidly. Based from ITU data, by the end of 2007 almost one out of two people had a mobile
phone. Specifically, in Europe, penetration has surpassed the 100% mark while more than
one out of 4 African and one out of 3 Asian have a mobile phone. A high level of competition
and a decrease in prices have been able to reduce the so-called digital divide in mobile phone,
substantially.
Figure 1.2 shows the three application of ICT: landline phones, mobile phones and internet.
The penetration rates of these three applications are then compared among the four regions,
namely: Africa, America, Asia, Europe and Oceania. Among the five regions, Europe has the
largest penetration rate of ICT applications, except for internet subscription where Oceania
lead the degree of ICT penetration. The Oceania region followed the next highest ICT
penetration rate placing the America on the third rank, where they equaled the internet
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penetration rate with Europe region. Currently, in Asia, the number of mobile phone
subscription is evidently growing, while landlines remain at the penetration rate of 5 per 100
inhabitants, placing fourth in the rank of the world (ITU World Telecommunication, 2008).
The limited availability of landlines has also been a barrier to the uptake of fixed broadband
and it is most likely that Asia’s broadband market will be dominated by mobile broadband,
with the exception of the developed countries in Asia, of course. Internet use, in general,
remains low in Asia especially, where only 14 percent of the population is online, compared
to over 40 percent in Europe, the Americas, and Oceania.
Figure 1.2 ICT penetration around the world, by regions, 2007
Source: ITU World Telecommunication, 2008
Even within the developing regions, the penetration of ICT is in different levels across
countries. As depicted in Figure 1.3, telephone lines (or landlines) are very much uncommon
in the developing countries, such as, Indonesia, Philippines, Malaysia, Thailand and even
China. Compared to the developed countries like Singapore, Japan, UK, Germany,
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72
4152
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14
148
110
41
114
79
45
0
20
40
60
80
100
120
140
160
landline phones mobile phones internet
Africa AmericaAsia EuropeOceania
Pene
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rate
per
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inha
bita
nts
7
Switzerland and USA, landlines subscribers are greater than the mobile phones in developing
countries. Most interesting to know is that in the Philippines subscription of mobile phones is
higher than telephone lines. This entails that access to mobile phones is likely to be more
important for individuals as a tool for communication in Philippines. The main reason for this
is because it is less expensive to acquire mobile phones than having telephones lines. Second,
it is because mobile phones are too handy and convenient to use (Pica & Kakihara, 2003).
Figure 1.3 ICT penetration by country (in the developing and developed countries)
Source: ITU World Telecommunication, 2008
Apart from less expensive and handy, mobile phone has the short-message-service (SMS),
also known as text in the U.S. or mobile phone email in Japan. SMS is found to be the
cheapest and popular form of communication in the developing countries. It is reported that
an estimate of about $100 billion with 3.3 billion cell phone users all over the world spent 1.7
trillion text messages in 2007 (IFPI, RIAA, MPAA, 2008). In fact, as shown in Figure 1.4a,
5543
104 91
69
169
124
174 167183
175
137
59
35
8880
41
127
84
118 115 118108
84
6 6
60
21 16
70 69 66 6451
6272
0
20
40
60
80
100
120
140
160
180
200telephone subscribers per 100 inhabitants
mobile cellular subscribers per 100 inhabitants
internet users per 100 inhabitants
Pene
trat
ion
rate
per
100
inha
bita
nts
8
the combined global Hollywood box-office receipts, global music sales, and US video-game
and PC game revenues came to only about half the US $130 billion that short message
service (SMS) brought in 2008, according to the market research firm Informa Telecoms &
Media.
In Figure 1.4b, with a fourfold increase in the past two years; even though, the
United States came late to the SMS revolution, its total texting now settles it second only to
China. Countries such as the UK, the Philippines and Japan also report extensive use of text
messages (Ling, Julsrud, & Yttri, 2005).
Figure 1.4 Illustrates the statistical data for SMS:
a.) Shows the SMS revenue in 2007.
b.) Presents the top texters in 2007
Source: http://www.spectrum.ieee.org/oct08/6817
But the country second to none in its fervor to text is the Philippines. In 2007, Filipinos sent a
total of 155 billion text messages. That’s more than four text messages per day for every man,
woman, child, and baby. Filipinos even employed text messaging during the political
protests of the late 1990s, because it was then the only unmonitored mode of communication.
Filipinos sent about 1,707 text messages per person, by in 2007 (OVUM, 2007).
a) b)
9
By investigating and comparing the statistics of ICT and its penetration rate provide us a
clear distinction of the developing countries to the developed countries. Undeniably, more
researches regarding transportation and ICT-related impacts stemmed from the developed
countries. Aside from impacts on travel, ICT is also seen to have effects on social dimension
like it enhances social relationships or even build new relationships (Burkhardt & Brass,
1990), it reaches out people that spatially distant yet by using ICT makes them feel like they
are just virtually near. Communications breeds communication that is when people
Figure 1.5 Face-to-face and telephone sociability orientation (family versus friends) through life cycle
Adapted from Smoreda et al. (2001)
communicate, then he communicates more and communicating more sometimes leads to
participation that oftentimes lead to making travel. The ICT applications to be tackled in this
research is confined only to mobile phone use, internet use (sending emails and chat), and
landline use. When looking at communication technologies in everyday life from the
perspective of social uses of the technology, it is apparent that their utilization is embedded in
the social relations of an individual. Figure 1.5 is the illustration of residential telephone
usage from 1996 French study by Smoreda & Thomas (2001). It compares the amount of
calls sent to family with those given off to friends. that shows communication patterns go
together with the overall sociability orientation and vary at every life stages. It further
10
reveals a clear correlation between the life cycle stage and the preferred telephone
communication partners of a household. The study also shows that communication patterns
go together with the overall sociability orientation and vary at every life stages. In addition, it
indicates that ICT applications like the electronic communications are associated with face-
to-face contacts. Landline phone as well as mobile phone contacts shows in relations with
family and friends, the rule is “The more I see you, the more I call you” (Smoreda & Thomas,
2001). The aforementioned result underlines the strong link between the number of persons
met in his social network and the number of phone calls made to the same number of persons
met. This holds true in the study of Dimmick & Patterson (1996) where the different levels
of affective and physically “proximate” relation lead to different levels of telephone use.
There are various benefits of ICT. The benefits listed below are the general advantages that
can ICT can offer in different aspects. To further enumerate the applications and benefits that
ICT can offer, here are the following:
1. Social connections.
2.
For example, research has shown that over 60 million
American citizens turn to the Internet when they need career advice, helping
people through an illness or finding a new house (Anderson, 2008).(Anderson,
2008). It also shown that the internet has become a basis when searching for vital
information. Many websites cater as a tool to interact and communicate with old-
established friends over online connection. Although there is still a need to be
really vigilant on the accuracy of the information on the web. Even though it
appears as a 'fact' on a web page somewhere, does not mean that it is true. Good
judgment is even more vital as you try and sort out the dross from the good
information.
Friends and family. No matter when or where they are, people can talk to one
another if they have access to the
3.
proper technology (Anderson, McWilliam,
Lacohée, Clucas, & Gershuny, 2007).
Travel and the environment. Through video conferencing and email, for example,
the necessity to travel is reduced. This has allowed people to have more time at
home with their families rather than being trapped in an airport somewhere. Less
11
travel also indicates less pollution, since fewer cars and aircraft need to be used
(Yi & Thomas, 2007).
4. Working anywhere. Being able to access the company network from anywhere
means that people are no longer tied to the office, they could just as easily work
from
5. E-commerce. The use of ICT is practically associated with the ability of firms to
innovate. In addition, ICT has helped facilitate the innovation practice, for
example, by speeding up information, to automate business processes or widen
access to information via internet. This way ICT promotes networking, which
enables cooperation between firms. Firms that have already innovated often
achieve better results from ICT than those that have never innovated (Hempell,
Leeuwen, & Wiel, 2004).
home (Mokhtarian, 1990). Because of this, home working ('teleworking') is
becoming more widespread. For example, people working for international
corporations can travel from country to country on business and yet settle down
to a fully networked local office desk and work as if they are in their home office.
This has also an impact on travel and environment.
6. Other benefits include education and training, in which video conferencing and
remote control of another computer has allowed teachers and trainers to run
lessons from a distance. For example, a multinational corporation located in the
UK wants to train their staff located in the Philippines on a new computer
application. Normally, the Filipino staff would have to come to the UK for
training. But now, the UK office can set up a video link with the Philippine office,
in which a remote control of the PCs in the Philippines and they run the training
course directly from the UK, where both sides benefit. Lastly, ICT is able to
spread concerning on the world awareness. The 24 hour news network brings us
events from around the world as they happen. This means that as a society we can
react almost immediately, through ICT applications, i
n natural disasters such as
tsunami or massive aid from nations from around the world (Kirschner & Paas,
2001).
12
1.1.2 Fundamentals of social dimension
Man as a social being represents the highest level of development especially in the social
forms of life, communication and consciousness (Spirkin, 1983)
. This may be the reason why
discretionary or social purposes account for a third to a half of total personal travel (Anable,
2002; ECMT, 2000; Götz, Loose, Schmied, & Schubert, 2003). Table 1.1 presents the
distribution of trips according to its purpose from the 2001 national household travel survey
in USA. It is clearly depicted that a large portion of daily trips are taken for family and
personal reasons such as shopping, running errands, and recreational activities of age group
19-64. Likewise with social and recreation trips, such as visiting friends, accounted for the
largest percentage of older adults’ trips, about 19% (Collia, Sharp, & Giesbrecht, 2003).
Based from this data, it signifies that people’s travel is almost always attached with some
social dimension that we cannot just simply disregard and neglect it. Therefore, social
dimension plays an essential part of the daily undertakings of the person’s life.
Figure 1.6 Activity type
Adapted from Silvis and Niemeier (2006)
13
Although several researches have studied social dimension, most of them tackled more on the
activity participation component specifically focused on trip duration of every purpose of
activity travel (for example, Golob & McNally, 1997; Goulias, Barbara, & Kim, 2005; Silvis
& Niemeier, 2006). Silvis & Niemeier (2006), as shown in Figure 1.6, illustrate and compare
the time duration for each activity type. The highest time duration allotted is mainly for social
activity purposes followed by personal activity. It even exceeded the mean when it is
compared to work or school, which has lower than the mean. The illustrations in Table 1.1
and Figure 1.6 presented clear evidence on the rational importance to study and incorporate
the social dimension in the travel behavior analysis. The social dimension has been
overlooked in the previous decades but lately it has gained a little attention ever since the
coming of the new technologies.
Table 1.1 Daily travel: Distribution of trips by trip purpose
Trip Purpose
Age 19-64
Percent SE
Work/work related
a
16.1 0.15
Shopping 13.2 0.14
Family/personal business 16.4 0.15
School 0.9 0.04
Religious 1.3 0.04
Medical/dental 1.3 0.04
Social/recreation 17.1 0.15
Return Home 32.7 0.10
Other 1.0 0.04
Total 100.00 -
Source: The 2001 National Household Travel Survey, Daily Trip File, U.S. Department of Transportation.
a SE denotes standard error.
14
Social activity has been defined as activity considered appropriate on social occasions
(WorldNet, 2010). In the statistics of Canada, for example, the social activity participation is
considered as one of the indicators to well-being and is even classified into several categories.
As illustrated in Figure 1.7, the classifications of social activities are not mutually exclusive
as respondents were able to state that they participated in more than one category of the
organization. Participation in social activities is a significant component of well-being
of
Figure 1.7 Participation in social activity groups, by types of organizations, 2003
Source: Statistics Canada. 2003 General Social Survey on Social Engagement
people and their ability to socialize with others (Statistics-Canada, 2010). Being socially
associated with other people and with social institutions, promotes social interaction, helps
enhance the value of belongingness to people, and offers balance life to people. Recreational
type of activity finds to be the most frequent engaged activity. However, in the preceding
decades, studies of activity behavior have been characterized as a dichotomy between “choice”
and “constraints”. This so-called dichotomy is even more noticeable characteristics of activity
than actual. Moreover, synthesizing the two courses has been uncommon. Most presented
empirical research, which incorporates activity-related concepts, hardly sheds light on the
causality underlying that behavior that it often tells more about observed sequences of
5
6
8
16
17
18
25
28
0 5 10 15 20 25 30
Political
Other types
Fraternal/Service …
School/Community
Religious affiliated
Cultural/Educatio…
Union/Profession…
Sports or …
15
behavior (choice and constraints). While prior researches have been often helpful as a basis
of hypotheses, only a minute piece of the information generated is of direct use to analysts
who want to assess alternative approaches. Damm (1982) suggested five groups of activity-
related research which have emerged in the last decade: (1) spatial-temporal constraints; (2)
how decisions interact; (3) how members of a household interact; (4) isolation of critical
variables; and (5) multivariate analyses. Nevertheless, issues on social dimensions like the
frequency of social activities, social interaction and social network were dealt with slight
attention in transportation research. This was overlooked in the previous studies yet this has
been gradually found to be interesting in order to profoundly investigate the real root of
activity participation that would affect travel behavior.
Aside from social activities mentioned above, one part of the social dimensions that is dealt
in the earlier studies is the social interaction, which will be inclusively tackled in this research.
In sociology, social interaction is a dynamic series of social actions between individuals (or
groups) who modify their actions and reactions due to the actions by their
interactions partner(s) (Psychology Wikia, 2010).
Recent works discuss social interaction and travel are based in theoretical, mathematical and
even drafted it in simulation (e.g., Blume & Durlauf, 2002; Brock & Durlauf, 2003;
Stauffacher, Schlich, & Axhausen, 2005; Arentze & Timmermans, 2008). With the advent of
technology, social interaction is now often mediated by electronic devices, which does not
require co-presence but is altered by the introduction of a mediating technology. These
technology-mediated social interactions are further elaborated in the succeeding subsections.
Apparently, in this age of technology, a
pattern of exchanges and the coordination of interaction occurs in a social relationship. As
(Turner, 1988) formally emphasized that whenever one expects a greater need for predictable
responses denoting group involvement and activity, one must have the greater needs for
group inclusion among individuals in an interaction. He further characterized social
interaction into three element properties: motivational, interactional, and structuring.
Motivational processes are those that energize and mobilize actors to interact; interactional
processes relate to how actors use gestures to signal and interpret; and structuring processes
are those behaviors among motivated individuals that permit them to replicate and organize
interactions across time and space.
To better understand the dynamics of exchanges of social interactions, the fundamental
concept of social network analysis might be of help once integrated and applied. This is
16
because the patterns of separation and network formation among actors become apparent
from the exchanges. Moreover, belonging to social networks also helps to provide a number
of tangible benefits, including information, access to goods and services, and business
contacts, as well as emotional support. Social network may comprise of family members,
colleagues from work or friends in a social group (e.g, sports, academic, politics, etc.).
One
work associating social network to travel is done by Axhausen (2003) in which he
hypothesized that by looking into the social network of a person one can define his travel
behavior pattern. In transportation studies, the limited number of research on social networks
is probably due to the fact that only few datasets on social networks and social interactions
currently exist. Nevertheless, it has been recognized that research into social travel behavior
is important, as social activities are responsible for a substantial portion of travel (in terms of
trip frequency and travel distance, for example) and social activities are an important factor
of peoples’ well being (Berg, Arentze, & Timmermans, 2010). This concept helps gather the
thoughts in the entirety of the thesis to pursue and understand the effects of social dimension
to travel.
1.2 Research motivation
This thesis is mostly concerned on the use of ICT, social dimension of the respondents and
their impacts on travel behavior patterns. ICT is now widely used in the traffic system and is
expected to somehow alleviate vehicular traffic. Applications of ICT like mobile phone can
make people communicate in far-off places in an instant. Email can send long messages to
multiple set of friends and refresh old relationships. Internet, in general, will able people to
work online while at home. By embracing these new technologies, it would somehow change
the daily undertakings of people in several ways. Moreover, this research is motivated on
considering social dimension that has been overlooked in previous researches, which would
affect travel behavior especially in the age of technology.
The first motivating force that drives and keeps me pursuing this field of interest is on the
notion that travel behavior nowadays have significant interrelationship with the use of ICT.
ICT possesses wide applications including that of the field of transportation. As it has
17
become known to everyone, that ICT turned out to be more popular since late 1990s. It is
ubiquitous tool for communication, production etc. This part will be discussed in detail in
subsection 1.2.1.
Secondly, the growth of ICT in the developing countries has just commenced. As a result, the
adoption and use some ICT applications are currently mounting in developing countries.
Hence, the issues concerning ICT and travel are relatively new and fertile in terms of research
areas especially in the perspective of developing countries. As mentioned before, the
developed countries adopted ICT much in advance than the developing countries and most of
the previous studies related to the association of ICT and travel are initially set out for some
developed countries. Traditionally, the goal of ICT improvement was on increasing access of
people in developing countries to computers and to landline phones. However, these efforts
have almost been virtually overtaken by the immense growth of mobile phones in many
developing countries. Indeed, mobile phones are now the main mode of telecommunication in
developing countries (Dunstone, 2006) and they play the same role landline phones did in
facilitating growth. This issue is further elaborated in subsection 1.2.2.
Thirdly, aside from the use of ICT, we are motivated to pursue this study in order to explore
social dimension by relating it to travel behavior. Although it has just recently gained a little
attention, there are still gray yet fertile areas that have not been looked for investigation.
Along with the process of reviewing paper related to social dimension and travel, the area on
social interaction was found out to be limited in related resources. Hence, this study will
incorporate and look deeper in the area of social interaction. This is further discussed in
details in subsection 1.2.3.
The last but not the least motivating force of this study is the aspiration of coming up a travel
behavior modeling that can be utilized in developing and arriving new transportation
planning and policies consistent with the trend with the current time, which is the information
technology era.
18
1.2.1 The prospective role of ICT in transportation research
Does ICT affect travel? Ever since the advent of ICT, there are several applications that can
be a subject for investigation on its after-impacts. One of the main concerns of this study is
also to investigate on the effects ICT on the patterns of travel behavior. The trend of ICT
offers several useful intentions; in general it plays an essential role in education, commerce,
and production. Most importantly, ICT plays an important role in transportation studies (e.g.,
Mokhtarian, 1991; Janelle & Gillespie, 2004; Wang & Law, 2007). Specifically, it is
supposed to reduce traffic, for example, by using personal computer at home for work a
person need not go to workplace, hence work trip is reduced (Mokhtarian, 1990). ICT can
make communication convenient and at ease without having the person making travel.
Secondly, ICT may reduce pollution from emitted the vehicles (Mokhtarian, Handy, &
Salomon, 1995). Thirdly, ICT is said to be able to fragment activities hence it could lead to
more possibilities of multitasking (Lenz & Nobis, 2007; Alexander, Ettema, & Dijst, 2009).
Lastly, ICT also enhances and nurtures social relationships, for example, by constant
communication whether a call from mobile phone or an email through internet keeps the
relationship, old or new tied (Licoppe & Smoreda, 2005).
1.2.2 The vital role of ICT in developing countries
One of the thrusts of this study is about the investigation of ICT use in the developing
countries specifically on its affects on social dimension and their travel behavior, especially
in the Philippines where this study was undertaken. Much of the focus of the role of ICT
improvement was traditionally on increasing access of people in developing countries to
computers and to fixed-line telephones, often through regional tele- and IT-centres. The role
of ICT in the developing countries is vital. In most developing countries, landline phones are
scarce, expensive to have it install at home and therefore inaccessible. However, these efforts
have almost been virtually overrun by the explosive growth of mobile telephony in many
developing countries. Indeed, mobile phones are now the primary form of telecommunication
in developing countries (reference) and they play the same role landline phone networks did
in facilitating growth.
19
As depicted in Figure 1.8, very few landline subscribers are observed in the developing
countries compared to the developed countries. Upon the arrival of mobile phones, the cost of
acquiring it is less expensive compared to having landlines at home hence more people prefer
in acquiring it. Other than less expensive, mobile phones are also light weight and therefore
handy that it allows people to get connected anywhere and everywhere. In the developing
countries, mobile phones are more frequently used for immediate transaction for example
connecting to a family member if he has arrived home. In other cases, mobile phones are used
for business transactions like confirming if the goods from the agricultural land have reached
the market place or others used to synchronizing activities. Other applications of ICT like
personal computers or internet use are also in use in the developing countries although not as
comparable to developed countries.
Figure 1.8 shows three main indicators of ICT: 1) landline subscriptions, 2) mobile phone
subscriptions and 3) internet subscriptions. In 1997, landline subscriptions were still high
while mobile phone was fewer and while internet remains the fewest. However, in the
following years, the subscription of mobile phone and internet has rapidly increased while
landline remains constant until 2000. In 2001, this is the year when LATS (London Area
Travel Survey) data, which have been used in this thesis, was collected, an equal number of
subscriptions between landlines and mobile phones can be observed. Then, in 2002, mobile
phone subscription increasingly surpasses landline up until 2007. While in 2005, landline is
almost surpassed by internet subscription and it eventually surpassed the following year.
However, the penetration of ICT in the developing countries is quite slow compared to the
developed countries. Figures 1.9 to 1.11 illustrate the gap of ICT penetration between
developing and developed countries. Figure 1.9 shows the fixed telephone users from 1997
up to 2007. By comparing the subscriptions, still the developed countries have greater
subscriptions than the developing. However, the gap between developed and developing in
1997 is wider and becomes closer in 2007, from a ratios of 3.87 to 2.63. Figure 1.10 shows
the mobile subscriptions between developing and developed countries. There is a big gap
between developing and developed countries in terms of mobile subscriptions. However, this
20
Figure 1.8 Fixed telephone subscribers
Source: ITU World Telecommunication, 2008
Figure 1.9 ICT subscribers per 100 inhabitants: Fixed telephone subscribers
Source: ITU World Telecommunication, 2008
68 7369
22
74
146
13
71
84
0
20
40
60
80
100
120
140
160
97 98 99 00 01 02 03 04 05 06 07
Fixed line per 100 inhabitants
Mobile phone per 100 inhabitants
Internet per 100 inhabitants
54 54 56 57 57 56 55 54 53 52 50
14 14 15 16 17 18 18 19 20 20 19
0
10
20
30
40
50
60
97 98 99 00 01 02 03 04 05 06 07
Developed countries
Developing countries
Year (1997-2007)
Year (1997-2007)
Year (1997-2007)
Pene
trat
ion
rate
per
100
inha
bita
nts
Pene
trat
ion
rate
per
100
inha
bita
nts
21
Figure 1.10 ICT subscribers per 100 inhabitants: Mobile phone subscribers
Source: ITU World Telecommunication, 2008
Figure 1.11 ICT subscribers per 100 inhabitants: Internet users subscribers
Source: ITU World Telecommunication, 2008
1825
35
5058
6570
7786 90
97
4 5 8 12 16 19 22 2834
4149
0
20
40
60
80
100
120
97 98 99 00 01 02 03 04 05 06 07
Developed countries
Developing countries
1117
2431
3642
46
54 56 59 62
2 3 5 7 8 10 12 14 15 1822
0
10
20
30
40
50
60
70
97 98 99 00 01 02 03 04 05 06 07
Developed countries
Developing countries
Year (1997-2007)
Year (1997-2007)
Pene
trat
ion
rate
per
100
inha
bita
nts
Pene
trat
ion
rate
per
100
inha
bita
nts
22
Figure 1.12 Internet subscription between UK and the Philippines
Source: ITU World Telecommunication, 2008
Figure 1.13 Landline phone subscription between UK and the Philippines
Source: ITU World Telecommunication, 2008
1.1 1.43 1.98 2.52 4.33 4.86 5.24 5.4 5.74 5.97 6.2213.67
21.2926.82
33.48
56.4860.82 62.69
66.37 65.5771.87
76.24
0
10
20
30
40
50
60
70
80
90
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Philippines
United Kingdom
1.28 2.5 4.18 4.51
47.3652.71
58.4754.24
0
10
20
30
40
50
60
70
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Philippines
United Kingdom
Year
Num
. of
subs
crip
tion
s pe
r 10
0 in
habi
tant
s N
um. o
f su
bscr
ipti
ons
per
100
inha
bita
nts
Year
23
gap is reduced in the year 2007. Figure 1.11 illustrates the internet users between developing
and developed countries. As you can see, there is an increasing pattern of internet subscribers
from year 1997 to 2007 despite the decreasing gap. Yet, in the developing countries there is
still small number of internet subscribers. As illustrated in Figure 1.11, the use of internet is
increasing from 1997 to 2007. However, the gap of internet subscribers between developed
and developing countries become larger in the year 2007.
By taking internet subscription rate and the landline phone penetration rate between UK and
the Philippines as an example, we can identify the clear cut between developed and
developing country. As shown in Figures 12 and 13, there is large gap of internet and landline
phone subscribers between the UK and the Philippines, where UK has higher penetration rate
for both internet and landline phone subscription than the Philippines.
1.2.3 Social dimension in transportation studies
This research also concerns about the investigation of the inclusion of social dimension into
the travel behavior analysis. The concept of social dimension in the context of travel behavior
has just recently commenced. Long before the analysis of travel behavior, social dimension
has been mostly dealt in the area of sociology. This study focuses on the social dimension
particularly on social activities, social network and social interaction. The inclusion of social
activities was performed by some early transportation studies like Lu & Pas (1999). On the
other hand, the inclusion of social network in the travel behavior analysis was initially
investigated by Axhausen (2003) and Carrasco et al. (2006) among others. However, social
dimension and social interaction have not been intensely explored and applied in travel
behavior analysis. In the context of sociology, social interaction has been defined as a
situation where the behaviors of one actor are consciously organized by, and influence the
behaviors of, another actor, and vice versa (Turner, 1988). Social interaction might have a
vital role in transportation. Sociologist Georg Simmel (1907) argued that people travel for
two reasons: 1) being attracted to each other and 2) enjoying sociability in the form of social
interaction. Arentze & Timmermans (2008) did some works on micro-simulation having an
assumption that the utility of a person derives from social interaction is a function of the
dimensions of social and information needs to be satisfied in the interaction. In order for the
social interaction to be fulfilled, there must exist some degree of similarities between persons
24
who involve in terms of their attributes and preferences, while satisfaction of information
needs for which social interactions tend to be instrumental depend on cognitive factors.
1.3 Research objectives and scope
This study primarily focuses on the establishment of interrelationship among travel behavior ,
the characteristics of ICT use and the social dimension. Empirical results analyzed
throughout this thesis should prove to behave soundly in order to incorporate selected key
issues of travel behavior patterns on social dimension and ICT use.
The general objective of this study is to develop a conceptual framework of travel behavior
by investigating and incorporating the impact of ICT use and its effects on social dimension.
By doing so, this study will be able to enhance the previously suggested travel behavior
model that existed before the apparent growth of ICT.
Followed by the above-mentioned general objectives, the specific purpose of this study is
firstly to investigate ICT use particularly on the effects of mobile phone possession and
telecommuting on the daily weekday trips and tours. Secondly, this study aims to examine
the influence of ICT on the social dimension that consequently would affect travel patterns.
ICT here includes mobile phone use and computer use (e.g. email and online chat). This
study, specifically, investigates on the effects on social interaction, social activities and social
network in the context of developing countries.
Time planning and ICT use has been rarely dealt in the previous studies. For this reason, this
hopes to examine the effect of ICT use on time planning of social activities
Finally, as a form of validation to the resulting model, this research is also aimed to compare
the effects of ICT from the developing country perspective (for example, the Philippines in
the case of this study) to the case of the developed countries (UK in this case).
25
1.3.1 Scope of research
This research has encountered several drawbacks and limitations during the process of the
analysis. Here are some of the following limitations:
1. ICT use – This study focuses mainly on the frequency of mobile phone use and
internet use, particularly, sending emails and online chat. The reason for
narrowing the considerations to those mentioned above is that it is the most
frequently used ICT application in the Philippines, where the study was
undertaken. With regards to the characteristics of mobile phone, it includes
questions on possession as well as the frequency of its use. However, for the UK
data only mobile phone possession was asked. This makes the comparison
between the effects of mobile phone restricted. In addition, the concept of
telecommuting was not asked in the Philippines. Other ICT applications are opted
not to include but would gladly to include in the future works.
2. Comparison scope (developed vs. developing countries) – This study hopes to
focus more on the developing countries side. The Philippines as the chosen target
study area is regarded as a representative study for the developing countries.
Since ICT applications are currently and rapidly growing in the developing
countries, it is timely to take the opportunity to the study the effects it will cause
especially in the area of transportation. As a representative for the developed
countries we regarded the data of United Kingdom (UK). The situation when data
was collected from UK in 2001 would nearly replicate the current situation of
ICT growth in the Philippines. In addition, the findings also are compared to the
developed countries when they first embrace ICT applications
3. Social aspects – As for the sets of data collected, the UK data did not include
attributes from social dimensions social network information, frequency of social
interaction. Hence, the part of social characteristics did not cover the comparison.
26
1.3.2 Significance of research
The main contribution of this dissertation is the inclusion of several factors affecting travel
behavior. These factors include but not limited to social dimension and ICT use. The
contribution of this study is enumerated as follows:
1. I contributed to the effect of ICT on travel in the experience of developed
countries.
2. I defined the amount of time of telecommuting needed to cause a shift of travel
behavior pattern.
3. I contributed to the effect of ICT on tour numbers and tour complexity.
4. I developed an empirical model directed from a developing country experience to
investigate the causal relationships of ICT use, social dimension and travel. I
believe that this will be the pioneering study in the developing country
concerning ICT use and social dimensions.
5. I introduced social dimension in the travel behavior analysis, specifically social
interaction. In the previous studies, social interaction was not fully discussed in
details.
6. I developed a customized method of collecting social network suitable for the
university students and workers. Although Carrasco et al. (2006) suggested the
method of collecting social network, i.e., the ego-centered approach, in order to
study travel behavior pattern but then the complicated method might get the
respondent mixed-up. Hence, a more customized method is newly made in the
context of the developing countries, particularly in the context of respondents
from the academe.
7. I expanded the conceptual model on travel behavior analysis by (Lu & Pas, 1999)
by adding ICT use and particularly on the time planning of social activities.
Before ICT came, there was only socio-demographic characteristics and social
activity participation as part of the travel behavior analysis. However, the coming
27
of ICT would make some modifications of the analysis since ICT changes the
lifestyle of people.
8. I identified a particular ICT application that plays an important role in
transportation for the developing countries. The use of a certain ICT application
might be different in every country. The rate of mobile phone use might be
different in developing countries than in developed countries. Text messaging is
frequent in developing countries than in the developed countries.
9. I integrated the three social dimensions, i.e. social network, social interaction and
social network, in the analysis.
1.4 Definition of terms
Complementary effect
additional telecommunications generate
additional travel
Ego-centric approach
refers to personal data that are collected from
individuals
Face-to-face physical inter-personal interaction
Information and communications
technology (ICT)
an umbrella term that includes any
communication device or application,
encompassing: radio, television, cellular phones,
computer and network hardware and software,
satellite systems and so on, as well as the various
services and applications associated with them,
such as video-conferencing and distance learning
(SearchCIO-Midmarket.com, 2008).
Online chat
virtual interaction using online messenger through
internet
28
Name Generator elicits names for members of the respondent’s
(ego) circle of friends (alters)
Social activity has been defined as
activity considered
appropriate on social occasions (WorldNet, 2010)
Social dimension
refers to social interaction, social activities and
social network
Social interaction
is a dynamic series of social actions between
individuals (or groups) who modify their actions
and reactions due to the actions by their
interactions partner(s)
(Psychology Wikia, 2010).
Social network
defined as a set of actors (ego) and the ties (alters)
among them (Wasserman & Faust, 1994)
Substitution effect
trip that is totally eliminated
Telecommuting the use of personal computer at home for work
Text message
virtual interaction using short-message-service
(SMS) through mobile phone
Time planning
is defined here as the span of time during which
the traveler’s decision is made before engaging in
an activity
Tour
a tour that could comprise of one trip or a series
of two or more trips linked together
Tour complexity
a tour that comprises of at least 2 stops
29
1.5 Organization of the thesis
This thesis is partitioned into eight chapters as illustrated in Figure 9. For Chapter 1 and
Chapter 2, it is mainly related to the general background of the study and review of related
literatures. Chapter 3 and 4 pertain to the impact of mobile phone possession and
telecommuting in the case of London. Chapter 3 focuses on the frequency of trips while
Chapter 4 focuses on tour numbers and tour complexity. Chapter 5, Chapter 6 and Chapter 7
relate the inclusion of social dimension and ICT use which are analyzed in different aspects
for it is presumed to have effects in the travel behavior, in the case of the developing
countries. Conclusion and recommendations are elaborated in Chapter 8.
In Chapter 1, discusses the introduction and general background of the study. In this
introductory chapter, it provides some explanations on social dimension concepts and how it
relates to the theory of travel. This chapter also illustrates some statistical figures of ICT
applications between the developed and developing countries. In addition, the objectives of
the study are enumerated as well as the scope and limitations that this study experiences are
specified. The motivation that drives to pursue this study and the important contributions that
this study imparts to transportation society are also presented in this chapter.
Chapter 2 provides a review and enumerates the related literatures which are required to
comprehend the concepts in the later chapters. First, it will review on studies concerning the
fundamental sociological factors that might have an influence to making travels. Secondly, it
will also review on literatures pertaining to the use of technology that affects travel; for
example, the use of ICT.
Chapter 3 covers the analytical approach using the data of Londoners way back in 2001 when
the rapid growth of ICT just embarked in the developed countries. This study is performed
practically to investigate the possession of ICT to the frequency of trips on different type of
purposes. In this chapter, it intends to analyze based on ICT adoption of Londoners.
Chapter 4 looks at the potential effects of ICT on different types of tour and tour complexity.
This is chapter uses similar the data in Chapter 3. This is performed in order to determine the
ICT adoption of Londoners and tour characteristics it causes.
30
Chapter 5 presents the first conceptual model and the analytical approach employed. In this
chapter, the primary investigation of the conventional and ICT-mediated social interactions,
social network as it affects the pattern of travel behavior is performed. The investigation
focuses on the young cohorts represented by the university students in Metro Manila.
Chapter 6 addresses the second conceptualized model together with the theoretical technique
that was carried out in order to examine the effects of social activities as the intermediate
factor that affects travel behavior aside from other social dimensions. This chapter focuses
on the working population represented by the university workers in Metro Manila.
Conclusion and recommendations for further areas of research
Developing country: Philippines
Patterns of Socialization and Social Network
structure as Determinants of Travel
Social Activity participation as
intermediate indicator of travel
ICT use on time planning
and social activity travel
Chapter 5 Chapter 6
Chapter 7
ICT adoption and trips
Chapter 3
Developed country: United Kingdom
ICT adoption and tour complexity
Chapter 4
Introduction
Review of related literatures
Chapter 1
Chapter 2
Chapter 8
Figure 1.14 Structure of Research
31
Chapter 7 deals with the third hypothetical framework with similar empirical approach to the
preceding chapter. It is primarily on the investigation of time planning of social activity
travel that might have essential effects to social activity participation which could lead to
making trips.
Finally, Chapter 8 summarizes and enumerates all the important issues and implications
tackled in the previous chapters. Some potential applications of this study are enumerated.
This chapter also provides an overview of issues for recommendation and further study.
***
32
CHAPTER 2 LITERATURE REVIEW
2.1 Introduction
In terms of travel behavior studies, there are two major subjects to be tackled in this study:
ICT and social dimension. First is on the concept on effects of ICT, namely: substitution and
complementary; second, the subject on social dimension that includes social activities, social
network and some social interaction theories are broadly reviewed.
The discussion in this section will focus on the studies on the consequential effects of ICT on
travel and the theories of social dimensions employed in the travel behavior analysis. To be
specific, I concentrate mostly on the effects of mobile phone and telecommuting as well as
the effects of social dimension (like social activities, social interaction, and social network)
that just recently gained attention in the field of transportation research.
This chapter is divided into two parts: ICT and social dimension. The first part talks about
ICT. Subsections 2.2.1 and 2.2.2 take up the different effects of ICT. Section 2.2.1 deals with
the intensive review of the substitution effect of ICT on travel with particular focus on mobile
phone and computer-related activities, as ICT applications. The complementary effect of ICT
on travel is exhaustively discussed in Section 2.2.2. The second part tackles with regard to
social a dimension, which is further divided into 3 sections. Sections 2.3, 2.4, and 2.5 are
dedicated for social dimension. Section 2.3.1 deals with fundamental theories for analyzing
travel behavior emerge from social participation and activities. As of the moment, only a few
literatures encountered that deal with social interaction and travel and are summed-up in
Section 2.3.2. Then, in Section 2.3.3, it deals with the existing yet limited literatures that
relates social network to travel behavior. Some ICT and social dimension studies are also
taken into account in Section 2.4. Finally, summary and discussions are followed in Section
2.5.
33
2.2 ICT phenomenon and travel tendencies
With the advent of ICT, expectations remain high that it can help reduce physical travel and
negative effects of automobile travel (e.g., traffic congestion and air pollution). As far as the
available literatures are concerned, however, ICT applications and service could have
dramatic impacts and would provoke two profound possibilities on travel: substitution and
complementarity. First, ICT might affects travel by substitution. There are travels that
substituted with the incessant use of ICT. Some examples of ICT application that substitutes
travel are: teleconferencing, telecommuting, telebanking, tele-education or distance learning
and teleshopping. The second potential impact of ICT on travel is the complementarity effect.
This means that the more ICT is used the more travel is made. Numerous ICT applications
stimulating travel can be produced in which phone calls, emails, faxes, can trigger a trip.
The following subsections mainly discuss on the substitution and the complementarity effect
of ICT on travel.
2.2.1 Substitution Effect
Lee and Meyburg (1981) define substitution as a trip that is totally eliminated, on the contrary
to trips that are altered. The substitution effect on travel assumes the need for travel will
diminish as telecommunications will be used, instead. This effect for travel can be traced
back from the study by Kraemer (1982). He reviews some of telecommunication
technologies that have the potential for energy conservation. These technologies are video
conferencing, computer teleconferencing, audio teleconferencing and office automation –
refer to a host of technologies related to the handling of information and communication
among organizations similar to today’s so-called email. By his thorough examination,
Kraemer (1982) projects that by substitution of telecommunication for travel it will increase
the energy conservation.
The idea of substitution on travel is partly fortified by Salomon (1986). He reviews the
telecommunication and travel relationships. He suggested that there are some
telecommunications that projects to have a substitutive power of travel but actually it not only
diminishes a portion of travel (e.g. teleshopping where a buyer eliminates his trips but the
34
delivery of goods has to be made). He cited that telebanking is one of the promising
applications that has a great substitutive power.
He examines the aspect of working-at-home arrangement by having a small survey to a group
of employees of a computation center at a major northeastern university. The survey
consisted of 20 items in which the respondents were asked to rank their attitudes or opinions
on a variety of issues concerning work. There are a total of 39 collected data from the
participating respondents. He also stresses other possible effects of telecommunication. The
role of telecommuting, along with other telecommunications services, is examined and is
often suggested to be a solution to congestion-related transportation problems due to its
substitutive power on work travel. His paper reviews the problems of forecasting a complex
solution to social problems. It critically assesses the wide range of forecasting approaches
applied to tele-commuting and the reasons for the upwards bias. The appeals of the concept
combined with various interests are among the reasons for the optimistic forecasts.
Methodologically, forecasts of telecommuting tend to emphasize technological change while
underestimating the social implications which determine the adoption of such technologies.
A choice theory is suggested as an alternative approach which can address issues related to
human behavior in the context of technological change. The explanatory power of choice
models is demonstrated and suggested for future analysis of technologies which entail
extensive adaptation for adopters and institutions.
Senbil and Kitamura (2003) performed a survey in Osaka metropolitan area of Japan to
explore the relationships among the use of home and cellular telephones, activity engagement
and travel. The analysis has shown that home phones and cellular phones have different
effects on activity engagement because of the functional differences between the two.
Statistical results suggest that substitution effects prevail between telecommunications and
travel when work activities are concerned.
Mokhtarian (1990) classifies some examples of telecommunications that has the potential to
substitute travel, that is, telecommuting, teleconferencing, teleshopping, telebanking, tele-
entertainment. She conceptualizes the relationship between telecommunications and
transportation as seen in Figure 2.1. It illustrates the principle of potentially simultaneous
substitution and generation: the actual amount of personal travel increases as part of a general
expansion in telecommunication, even though its share declines. The situation depicted is
35
Figure 2.1. Relative substitution among communications modes, simultaneous
with absolute expansion of all modes
Adapted from Mokhtarian (1990)
one in which the personal travel and information object transmission modes lose share to
electronic communication over time, but in which the continued expansion of communication
by all modes results in the absolute amounts of communication being greater for all modes at
the later point in time than at the earlier point.
Handy and Yantis (1997) study focuses on the implications of telecommunications for non-
work travel and explores the potential substitution of in-home versions of an activity for out-
of-home versions of that activity. There are three specific activities are selected to represent
the spectrum of non-work activities from entertainment to personal business; for instance,
movies (theater vs. VCR vs. television), shopping (store vs. catalogue vs. television), and
banking (bank vs. ATM vs. phone vs. online). A household survey was implemented to
characterize the use of the different versions of the three case study activities and explore the
trade-offs between them. Using factor analysis, the results of the 3000 household samples
personal travel
information freight
electronic transmission
36
(within San Jose, CA, Oklahoma city, OK and Austin, TX) suggest a complicated
relationship between in-home and out-of-home versions of an activity depends on the nature
of the activity and the characteristics if the individuals. So far it appears that out-of-home
versions of movie-watching, shopping, and banking offer qualities that are not currently
duplicated by the in-home versions, and that these qualities are important for most individuals
some of the time and for some individuals most of the time. At the same time, the results
show signs that as technologies and services improve the degree of substitution may increase.
In-home versions of banking seem to serve both to reduce trips and to increase the number of
transactions, especially those related to acquiring information. At the same time, the results
show signs that as technologies and services improve the degree of substitution may increase.
ICT applications like telephone or mobile phone are not found to have a substitutive effect on
travel. Instead, they possess a complementary effect.
2.2.2 Complementary effect
According to Salomon (1986) complementary means additional telecommunications generate
additional travel between two node, which would not occur had there not been a
communications channel (Salmon, 1986). He suggests that mobile communication
technologies (e.g. mobile phones) best exemplify the case of telecommunications
complement the transportation system after making a review on the relationships between
telecommunications and travel. He also include, in his review, the analysis of some
applications of telecommunication technology for remote work (or telecommuting),
teleconferencing, teleservices, mobile communications and electronic mail transfer however
these are unlikely to exhibit a complementary effect on travel.
In addition, Fadare (2003) looks at the impacts of telephone uses of residents in Osogbo,
Nigeria on the travel behaviour, particularly within the realm of the three popular
telecommunication propositions of substitution, inducement and complementarities. The
study is based on a randomly selected set of 163 households with functioning telephones.
Evidence from the study shows that the usage of telephone in the study area tends to increase
the number of trips.
37
Conceptual, theoretical, and empirical evidence with respect to the impact of
telecommunications on travel is further examined by Mohktarian (2003). The primary focus
is mainly on passenger travel, but goods movement is addressed briefly. With confidence,
their findings say that the empirical evidence for net complementarity is substantial, although
not definitive.
Krizek et al. (2005) analyzed the pattern of substitution affect between traditional and ICT-
form activities and the affected attributes of people making a choice whether or not to
substitute. This study formed three groups in conducting a survey: (1) asked the general use
of ICT in eliminating trips (2) asked the people to think about their last use of a particular
activity and speculate what they have done if that version had not been available (3) aims to
understand the motivations for physical shopping. The paper concluded that ICT provides
convenience, efficiency and abundance of information; it does not necessarily reduce travel.
Zhang et al. (2005) present an empirical examination of the relationship between ICT and
travel. The ICT indicators they employ include the frequency of Internet use, the number of
mobile phones, and the presence of a telephone at home for business purposes. The travel
outcomes examined are vehicle miles traveled (VMT), total daily trips, and daily walking
trips. Using the 2001 national household travel survey (NHTS) data for Baltimore
metropolitan area, a linear regression model is estimated for VMT and two Poisson
regression models are estimated for total daily trips and daily walking trips, respectively. The
empirical results suggest simultaneous existence of substitution and complementarity
interactions between ICT and travel, with complementarity as the dominant form.
Srinivasan and Raghavender (2006) investigate the influence of mobile phones on three
travel-related dimensions: unplanned activity-chaining and unplanned ride-shares arranged
using mobile phones, and shopping over phone. Using data from 400 workers in the Chennai
city, the results reveal that mobile phones appears to be strong and has complementary effect
the above travel dimensions as well as to activity participation. Their results also provide
evidence that social connectivity, activity characteristics, mobile phone use, and travel
patterns are all strongly interlinked.
Choo and Mokhtarian (2007) focused on the relationship between telecommunication and
travel from the economic point of view using the national time series data spanning 1950–
38
2000 in the US. The number of phone calls is assumed to be a measure of telecommunication
and VMT as the measure of transportation. Using the structural equation modeling,
aggregate relationship between telecommunications and travel is complementarity.
Wang (2007) uses structural equation model (SEM) to analyze the impacts of ICT usage on
time use and travel behavior. The sample is derived from the travel characteristic survey
conducted in Hong Kong in 2002. The usage of ICT is defined as the experience of using e-
mail, Internet service, video conferencing and videophone for either business or personal
purposes. The results show that the use of ICT generates additional time use for out-of-home
recreation activities and travel and increases trip-making propensity. The findings of this
study provide further evidence on the complementarity effects of ICT on travel, suggesting
that the wide application of ICT probably leads more, not less to travel.
Some case-specific studies on complementary issues of ICT are also found in the succeeding
sections and are discussed more appropriately.
2.3 ICT and effects on time planning
Up to this day, very limited resources can be found that tackles the issues on time planning
due to ICT use and travel. The latest most relevant state of the art study is performed by
Hjorthol (2008). She explores the relationships between aspects of time norms, planning of
everyday activities, use of a mobile phone, and the car in families with children. The analysis
is based on results from a survey with a random sample of 2000 respondents from families
with children in Norway, 2005. The analysis shows that the mobile phone is very important
in everyday communication among family members. Short planning time and use of the
mobile phone go together. The general level of car use varies with planning horizon and the
choice of the medium used for arranging and rearranging appointments. There is a relation
between high frequency of car use, a short planning horizon and use of the mobile phone.
39
2.4 Sociological approaches to analyze travel
2.4.1 Social activity participation
In the early years, the sociological approaches received only a little attention in the
transportation research arena. Just when the ICT has become ubiquitous all over the world
social dimension simultaneously becomes attractive for travel behavior research. Although,
the root of discussion all started from the concept of including activity patterns but other
sociological aspects are not covered yet or included in the analysis. In order to best represent
the sociological approach in the travel behavior analysis, it is better to deal with apparent
social dimension collectively, that is, incorporating social activities, social interaction and
social network.
Travel behavior models started with the analysis of individual behavior then eventually
expanded to aggregate model. Albeit exhaustive researches that are continually performed to
capture the best model, there are still possible factors that generate travel remain to be
explored.
According to van der Hoorn (1979), trip generation can be captured by considering the
common activity pattern of individuals or households as the departure point and considering
travel as a derived demand. To prove his argument, he conducted a survey for approximately
1100 persons in the Netherlands. Diaries are kept to record their main activities every quarter
hour during the week. The study initially employs 10 broad activity groups that later on
narrowed down to five distinguished groups upon successive technique, namely: work,
housekeeping and children care, shopping and personal business, study, and culture all
together with social visits, active and passive recreation. Based from their results, the
percentage of work trips and study trips diminish when short trips are taken into account
while shopping, leisure and active recreation percentage increases. The population is divided
into five person categories, and the travel pattern and activity pattern are studied separately
per person group.
Golob and McNally (1997) employed the structural equation model to explain activity
interactions between heads of households with the aim of explaining household demand for
travel. The model attempts to capture links between activity participation and associated
40
derived travel, links between activities performed by male and female heads, links between
types of travel, and time-budget feedbacks from travel to activity participation. Data used are
from the 1994 Portland Activity and Travel Survey. The results suggest that a feedback
mechanism should be introduced in trip generation models to reflect the effect of activity
frequency and duration on the level of associated travel. By utilizing the activity-based
approaches, it enhances their understanding of travel behavior via the development of models
of scheduling and activity participation and the examination of the relationships between
household members, their activity demands, and the constraints that bind their decision
processes.
Lu and Pas (1999) describes the development, estimation and interpretation of a model
relating socio-demographics, activity participation (time use) and travel behavior, as
illustrated in Figure 2.2. A complex set of interrelationships among the variables of interest
is estimated simultaneously using the structural equation model, with activity participation
and travel behavior endogenous to the model. Their study shows that complex relationships
exist among socio-demographics, activity participation and travel behavior. Particularly, the
results show that to better explain travel behavior activity participation should be included in
the model, rather than through purely socio-demographics characteristics. Furthermore, the
results indicate that there is a relationship between in-home and out-of-home activity
participation and travel behavior. Finally, their research demonstrates that by examining the
direct, indirect and total effects in the model system, it is able better to capture and to
understand the relationships among socio-demographics, activity participation and travel
behavior.
41
Figure 2.2 General model structure of travel behavior Adapted from Lu and Pas (1999)
Goulias and Hensons (2006) investigated the time allocation behavior in order to best
formulate and specify of activity analysis models to understand selfish and altruistic behavior
and relate this to travel behavior. The data from 1,471 persons in a recent 2-day time use/
activity diary and latent class cluster analysis is used to identify 11 distinct daily behaviors
that span from the intensely self-serving to intensely altruistic. The analysis shows strong
correlation exists between social role and patterns of altruistic behavior. However, a
substantial amount of heterogeneity is also found within social roles. In addition, travel
behavior is also very different among altruistic and self-serving time allocation groups. At
the household level, a substantial number of households contain persons with similar
behavior. Another group of households contains a mix of self-serving and altruistic persons
that follow specialized household roles within their households. The majority of households,
however, are populated by altruistic persons. Single person households are more likely to be
in the self-serving groups but not in their entirety. Altruism at home is directed most often
toward the immediate family members. This is less pronounced when we examine altruistic
acts outside the home.
Socio-demographics
Out-of-home activity participation
In-home activity participation
Travel behavior
42
Bhat and Lockwood (2004) examined the out-of-home recreational episode participation of
individuals over the weekend, with a specific focus on analyzing the determinants of
participation in physically active versus physically passive pastime and travel versus activity
episodes (travel episodes correspond to recreational pursuits without any specific out-of-
home location, such as walking, bicycling around the block, and joy-riding in a car, while
activity episodes are pursued at a fixed out-of-home location, such as playing soccer at the
soccer field and swimming at an aquatics center). Employing the disaggregation of
recreational episodes facilitates better analysis and modeling of activity-travel attributes, such
as travel mode, episode duration, time-of-day of participation and location of participation.
The disaggregation of recreational episodes provides important information to encourage
active participatory recreational pastime, which can contribute to a socially vibrant society
through increased interactions among individuals.
Goulias et al. (2005) utilized the CentreSIM survey which is an activity survey that allows
them to study behaviour from a different viewpoint and includes more than 1400 persons’
two-day activity/time use diary for entire households (including all their children). The
survey spans from November 2002 to May 2003 including weekends and holidays. In
addition to the typical activity information for each activity episode reported, each respondent
provided information with whom the activity was pursued and for whom. The answers to all
these items are analyzed in this paper to identify differences within a day and among the
different days of a week accounting for person and household characteristics. A variety of
homogeneous groups are identified and the determinants of different behaviours presented.
Significant differences are found in the two aspects analyzed, alone versus joint participation
and self-serving versus altruistic are observed among the persons that work in different ways
(part time and full time), among the different school age children, and persons that may
appear to have reasons to stay home. The disabled and the retired also appear to be very
active and diverse. The day of the week effects are very strong in this analysis with each day
having its own “character” in terms of activity participation.
Berg et al. (2010) use social interaction diary data collected in the Netherlands, estimation
using multinomial logistic regression model to analyze whether a social activity is pre-
arranged, routine or spontaneous as a function of personal and household characteristics,
social activity characteristics and characteristics of the contacted person. The results show
that the planning of social activities is significantly influenced by gender, presence of
43
children, education level, income and time spent on work and school. Social activity
characteristics were also found to have a significant impact. Social activities scheduled later
in the day are less likely to be routine. In contrast, social activities of longer duration and
taking place in the weekend are more likely to be routine or pre- planned. The location, the
main purpose of the social interaction and detailed characteristics of with whom the social
interaction took place were also found to significantly affect the scheduling process.
2.4.2 Social network structure
The increased ease of communication expands the size of our contact sets and therefore
increases the number of opportunities for face-to-face interaction (Mokhtarian, 2003). In
other words, with larger social network would probably mean larger opportunity to engage
travel. Urry (2003) considers the role that physical travel plays in social life. He noticed the
large and increasing scale of such travel that has occurred simultaneously with the
proliferation of communication devices that in some ways substitute for physical travel. He
then hypothesizes that the bases of such travel are new ways in which social life is
‘networked’. Such increasingly extensive networks depend for their functions upon
intermittent occasioned meetings. These moments of physical co-presence and face-to-face
conversation, are crucial to patterns of social life that occur ‘at-a-distance’, whether for
business, leisure, family life, politics, pleasure or friendship. He suggests that life is
networked that involves specific co-present encounters within specific times and places. He
termed it as ‘meetingness’, which different forms and modes of travel are central to much
social life - a life involving strange combinations of increasing distance and intermittent co-
presence.
This is in coherent with Axhausen (2003) as he observed that there seem to be an increasing
trend of spatial spread of social network at the same time a large increase of leisure travel.
For this reason, he has drawn a hypothesis that the travel pattern of a person is shaped by
structure of social network. In his paper, he made several experiments as hypothetical
evidence on travel. It is also in this paper that social network was first employed and
associated to travel.
44
Inspired from the assumptions made by Axhausen (2003), Carrasco et al. (2006) suggested a
method of data collection of social network designed to integrate the social dimension in
social activity-travel behaviour, explicitly studying the link between social activities of the
individuals and the structure of social networks. The make use of the data from the
Connected Lives Study in Toronto, June 2004- April 2005. Survey and a follow-up interview
to 84 people which elicited their personal network members (1019) and interactions with
them. With survey and interview instruments used, the data collects members of the social
network of the respondents through egocentric approach, constituted by the interplay between
their individual social structure and their social activity-behaviour. More explicitly, the
network of the individuals is studied including their relationship with social activity-travel
generation, spatial distribution, and the use of ICT.
Subsequently, Carrasco and Miller (2008) showed a social activity-travel generation model,
which explicitly incorporates social dimension of each individual through the concept of
personal networks, modeling the multilevel structure of social relations defined by these
networks. The paper uses a disaggregated perspective of personal networks to explicitly
incorporate the characteristics of each network member as well as the characteristics of the
overall social structure. The analysis uses the ordinal multilevel specification that accounts
for the social network in which individuals are embedded, four dimensions are studied:
personal characteristics, ‘‘with whom” activities are performed, social network composition
and structure, and ICT (information and communication technology) interaction.
Back then, Smoreda and Thomas (2001) compared communication technology use and
networks contacted. The study hypothesized that the characteristics of a network influence
communication structure, that is the adoption and the intensity of use. Technology profiles
were created that try to mirror the measures that showed up as important in the network
analysis: the size of the network, the communication means used, and the geographical span
of the network.
45
Figure 2.3 Information technology profiles of social network
(Adapted from Smoreda and Thomas, 2001)
In fact, the analysis revealed that network patterns and ICT use correspond for specific
combinations of technologies to a considerable degree. As depicted in Figure 2.3, the four
graphs compare the degree to which the different communication means are above or below
the grand mean for each of the four central dimensions of our scrutiny: the size of the
network, the percentage of friends, the percentage of family members in the network, and the
percentage of local members, i.e. of members contacted living in the same region. The more
the rhombus is vertically elongated, the more the part of the network contacted with the
specific communication means is large and localized. The more the rhombus peaks to the
right or the left, the more the network is composed of friends versus of family members.
Visits demand to overcome physical distance, to engage an effort that takes time. Therefore,
face-to-face meetings, which constitute, at the same time, the largest sub-network, are the
most localized. Also, they are more oriented towards the friends, i.e. they are more self-
%Friends
% Local
%Family
%Family %Family
%Family
% Local % Local
% Local
%Friends
%Friends
%Friends
number number
number number
1
2
11
1
2
2 2
mobile callsSMS
mobile callsSMS
mobile callsSMS
mobile callsSMS
Fixed and mobile calls
Emails and letters
Visits and fixed calls
Mobile calls and SMS messages
46
selected than the contacts with the family, which are norm-ruled and therefore, in part,
socially imposed. Mobile people make larger social network than the sedentary ones. There is
a strong relationship between the use of ICT and the face-to-face interaction. It would mean
that constant telephoning makes constant seeing each other which indicate a strong link
between the two nodes. The part of the network contacted through fixed line telephone calls
is smaller than the one for face-to-face contacts. As no physical effort to overcome distance is
demanded for establishing a call the network is less localized. The networks contacted via
the fixed line telephone are rather balanced between friends and family. The network
contacted by mobile telephone has structurally the same characteristics as the fixed line
network but it is even smaller, and more oriented towards the friends. The SMS-based
network exaggerates the aforementioned tendencies. It is the less oriented towards family
members and the most towards friends and it is far more restricted. Email and letter-based
sub-networks are, on the average, the smallest and the least localized. However, the average
covers two distinct sub-networks: one that is local, and the other, international. In spite of
these common traits, emails and letters are not sent to the same kind of person. Letters are
posted in similar proportions to friends and to the family, whereas emails are more often sent
to friends. This panorama gives us an insight in a potential specialization of the technologies
as a consequence of the type of communication partner.
A follow up paper made by Licoppe and Smoreda (2005) that use material from empirical
studies carried out over the last 3 years to develop hypothesis on forms of relationship change
with technology and to understand the relationship between social networks (a set of social
ties possessing one or more relational dimensions), exchanges between actors (made up of a
succession of embodied gestures and language acts) and the various technical means for
communication available today. As shown in Figure 2.4, each of the three poles poses
constraints on interaction, and provides resources for it, and all three shape the form
relational practices. Empirical data show the way technological means of communication
47
Figure 2.4 Media and timing of announcements by relational proximity of the correspondent
Adapted Licoppe and Smoreda (2005)
allow people to re-negotiate the constraints of individual time rhythms, and of who one
communicates with. It also illustrates how the relational economy (and power) is affected by
the deployment of communication technologies. Tools of communication provide new
resources to negotiate individual timetables and social exchanges, making it possible to adjust
roles, hierarchies and forms of power in relational economies. The traditional
communication model, where telecommunication is used to connect people who are
physically separated from each other, is gradually being supplanted with a new pattern of
“connected presence”. In this new mode other people are telephoned, “SMSed”, seen and
mailed in alternated way and small gestures or signs of attention are at least as important as
the message content itself.
On the other hand, Silvis and Niemier (2006) conducted a novel-survey designed to quantify
how social networks influence travel behavior. They distribute postcards to selected
households
telephone
fixed
mobile
Faire-part (written announcement)
(e-) mail
relatives
collective
Individualized simple
Via somebody
immediately
Sophisticated later
collective Individualized simple Sophisticated later Via somebody
Friends and acquaintances
48
Figure 2.5 Schematic of the iterative recruitment method Adapted from, Silvis and Niemer (2006)
and then gave them instructions to send them to the people whom they had contacted with for
over 3 days, as shown in Figure 2.5. Using a three-day activity diary, they simultaneously
measured social interactions and travel behavior. Respondents were asked to record all trips,
as well as all social interactions with friends and family for three days. The survey provides a
means of directly relating the characteristics of social trips to characteristics of the social
interactions they enable, rather than being limited to relating general information on social
networks to an individual diary of travel behavior. It is hypothesized that respondents would
travel longer times for greater social benefits, i.e., that people would be willing to travel
further to see more people, or to see people whom they have known for a longer period of
time. However, it is found that only the number of non-immediate kin at the destination
affected respondents’ trip duration. Additionally, they find that both the total number of trips
Seed group
Second phase
Third phase
Phase 1 July 18 toAugust 1, 2005
Phase 2August 15 to September 15, 2005
49
a respondent made and the number of different locations that a respondent visited were
closely correlated both with size of his or her social network and with the number of repeated
contacts. Individuals are willing to travel longer trips just for socializing. Habitual
interactions lead to greater number of trips while larger social network led to visiting more
locations.
Furthermore, Arentze and Timmermans (2006) argued that social networks are formed and
change over time in non-random ways and propose a framework to incorporate the dynamics
and impacts in micro-simulation of activity patterns. Propose a framework to incorporate the
dynamics and impacts in micro-simulation of activity patterns, that is, the utility a person
derives from social interaction is a function of dimensions of the social and information needs
satisfied in the interaction. The extent to which dimensions of social needs are satisfied is a
function of the degree of similarity between the persons involved in terms of their attributes
and preferences, while satisfaction of information needs, for which social interactions tend to
be instrumental, depend on cognitive factors. At the same time, persons tend to adapt their
preferences so as to increase the utility they derive from their social networks.
Simulations conducted to examine the behavior of the model. Here are the considerations in
carrying out the simulation:
1. A simulation run considers a time period of T days.
2. Each day, each agent considers sending out invitations and may receive
invitations from others to engage in an interaction.
3. An agent goes though the list of social contacts and calculates the utility of each
possible interaction. He sends an invitation to the best contact if the utility of an
interaction with this contact is larger than zero.
4. The receiving agent considers the invitation and accepts if his utility of the
interaction is larger than zero and rejects otherwise.
50
5. If the agent receives a positive response, the interaction is implemented and,
otherwise, he removes the social contact from his list and repeats the same
process.
6. This process continues until no candidate for a social interaction can be found
anymore.
7. If an interaction is implemented, both agents involved update their state variables
and the entire foregoing process is repeated.
8. Agents are processed in an arbitrary order. If the first agent has gone through the
protocol the next agent has his turn and so on.
9. Before going to the next day, the agents update their state variables and, in
particular, need size, link potentials and link strengths.
The patterns of interaction appear to be stable over time even though they are irregular. The
complexity results largely from the difficulty of synchronization of needs of persons within
networks. An interaction reduces the need and, hence, the coincidence of high need levels is
an important factor in the success of obtaining mutual agreement on starting an interaction.
The concept of social network is also applied to investment in friendship formation. This is
done by Brueckner (2006) in which he developed a model of social networks that considers
general network structure model or star network as well asymmetric network, as he called it
friendship network. The analysis, which is couched in the context of friendship networks,
shows that individual investment in friendship formation is too low. People do not expend
enough effort in forming friendship links. As demonstrated in Figure 2.6, the analysis shows
that, in an asymmetric setting where one individual has personal magnetism or a broad group
of acquaintances, friendship links involving this attractive agent are most likely to form. For
example, node 1 is the attractive node and in order for node 4 to get acquainted to node 3
51
Figure 2.6 Example of a friendship network: the knows everyone case Adapted from Brueckner (2006)
he/she has to befriend node 1 first. Virtual mobility is not a viable alternative for all people
for accessing all activities all of the time. There is an impact of age upon the propensity to
undertake different activities online. Similar concept was performed by Pinkster (2007)
pertaining to the social mobility of the neighborhood based from their social networks, job
strategies and work ethics.
Hackney and Axhausen (2006) assume a set of agents is placed on a transportation network
on which travelling has a cost. There are 65 agents used in the simulation. Micro simulation
of social networks in geographic space particularly agent simulation is employed for two
main reasons. First, information about the network context of activity planning is lacking.
Second, simulation can be used to build the needed global social network from assumptions
about the structure and the growth and decay processes of egocentric networks. The model
generates a global set of inter-household relationships based on dynamic ego networks that
develop with respect to travel opportunities. The results shows the dynamic social networks
can be generated with a random utility model for trip generation that is familiar in
transportation planning. It also shows the utility parameters influence the social network
topology and spatial exploration through the activity choices of the agents and the
indistinguishable agents, except for home address, interact with identical utility functions
across a periodic space with homogeneously expensive travel cost to generate social
connections with each other. The dynamics of meeting, learning about space, and therefore
54
1
2 3
52
the dynamics of the social network are simulated by the feedback through the activity choice
set, which is reinforced by the removal of links that are not re-visited and by gradual
saturation of agents with friends, as demonstrated in Figure 2.7. The model form provides a
basis for fitting to appropriate sample of activity-based travel behavior data. In this case,
indistinguishable agents, except for home address, interact with identical utility functions
across a periodic space with homogeneously expensive travel cost to generate social
connections with each other. The response of the model in social and geographic space to
Figure 2.7 Geographical layout of the model with social network centered on an ego
Adapted from Hackney and Axhausen (2006)
the travel cost parameter show intuitive as well as nonlinear sensitivity that makes the
simulation a rich experimental test bed. An orthogonal experimental design, which optimally
varies multiple parameters at once, has been defined and will be run to describe the
multivariate response surface. With the building blocks in place for generating the network
and analyzing it in view of the geographical constraints, the model can be expanded to
include a realistic set of activity purposes, negotiations between multiple participants per
activity, agent heterogeneity, and a set of locations that are not constrained to the residences
of the agents. Once these basic cornerstones of realism are in place, attempts can be made to
estimate the utility parameters using activity-based travel diaries.
Tillema et al. (2008) used the survey data collected among 662 respondents with the hope of
gaining more insights on: (1) the interaction between face-to-face and electronic contacts (2)
53
the influence of information content and relational distance on the choice of the
communication mode/service, and (3) the influence of relational and geographical distance,
in addition to various other factors, on the frequency of face-to-face and electronically
mediated contacts with relatives and friends. The result of bivariate correlation analysis
indicated that the frequency of face-to-face contacts is positively correlated with that for
electronic communication, which points at a generation effect. With respect to the impact of
information content and relational distance, it is determined that such synchronous
modes/services as face-to-face and telephone conversations are used more for urgent matters
and that asynchronous modes (especially e-mail) become more influential as the relational
distance or closeness in the social network increases. Lastly, utilizing ordered probit analyses
validates that both face-to-face and electronic communication frequencies decline with
increasing physical and relational distance to the social network members.
2.4.3 Theoretical models of social interactions
Blume and Durlauf (2002) describe the relationship between two different binary choice
social interaction models. They show that the equilibria of the Brock-Durlauf model are
steady states of a differential equation which is a deterministic approximation of the sample-
path behavior of Blume and Durlauf's model. Moreover, the limit distribution of this model
clusters around a subset of the steady states when the population is large.
Stauffacher et al. (2005) suggest that some psychological factors like personal need and
motives (e.g., social interaction, recreation, variety seeking and curiosity) are relevant,
especially for the highly individualistic behavior of leisure travel but have been largely
neglected in travel behavior studies. By employing two longitudinal diary studies of two-
and twelve-weeks duration in Switzerland, one in the city of Basel, the second in the
agglomeration of Zürich, the needs that specific leisure activities can satisfy and the role
social interactions play in leisure activity is investigated. It is found that social motives
dominate leisure travel, i.e. greater changes in travel demand can only be expected if people
reconstruct their social networks, e.g. living closer to friends and relatives.
Arentze and Timmermans (2006) introduced a framework for incorporating social networks
in dynamic micro-simulation of activity-travel patterns. They assumed in their theory that
54
similarity between persons in attribute, preference and action space increases the probability
that a link between them is created and sustained in time. Once a link is created, social
influence leads to knowledge exchange and adaptation of preferences. Thus, social networks
and activity-travel patterns of people tend to co-evolve. The result of the simulations indicate
that even under basic conditions, the patterns of social interaction emerging in the system are
already quite complex and stable at the same time.
2.5 Social dimension and ICT
There are several studies that address social dimension and ICT however for the purpose of
this study we narrowed down studies that closely related to the purpose of this study. To
mention, Perry et al. (2000) explores the relationship between mobile phone and activity
during business travel. The study find out that verbal communication are important for those
who are in business travel hence having mobile phone is valued.
Other studies simultaneously incorporate specific social dimension and ICT to study travel
behavior. For example, Mokhtarian et al. (2004) explores the potential impacts of ICT on
leisure activities. They discuss four kinds of ways by which ICT can affect leisure activities
and travel: 1) the replacement of a traditional activity with an ICT counterpart; 2) the
generation of new ICT activities (that displace other activities); 3) the ICT- enabled
reallocation of time to other activities; and 4) ICT as a facilitator of leisure activities. There
are 13 dimensions of leisure activities presented that are especially relevant to the issue of
ICT impacts on horizon, temporal structure and fragmentation, possible multitasking, solitary
vs. social activity, active vs. passive participation, physical vs. mental, equipment/media
(in)dependence, informal vs. formal arrangements required, motivation, and cost. It is
determined that the primary impact of ICT on leisure is to expand an individual’s choice set;
however whether or not the new options will be chosen depends on the attributes of the
activity (such as the 13 identified dimensions), as well as those of the individual.
55
Carrasco et al. (2006) also included in his study the effects of ICT on travel in which travel
includes those stimulated by social activities. However, the causality is still unclear on
whether the impact is due to travel or social activities.
2.6 Summary and discussion
This chapter recapitulates the existing sophisticated literatures relevant to this study most
importantly on the effects of ICT applications: substitution and complementary. The existing
studies that tackles social dimension together with travel is also reviewed, especially on
social activities, social network and fairly on social interaction due to its unsubstantial
resources. The theoretical and the empirical methods employed, in the literatures reviewed,
that determine whether ICT or social dimension affects travel are included in the discussion.
Upon the review of the relevant literatures, this thesis is found to have several key issues that
make it a distinctive transportation study. The key issues that make this study unique are
enumerated as follows:
1. This study examines the interrelationship among ICT, social dimension and travel
behavior that the aforementioned studies skipped to analyze. These three factors
might exhibit a significant interrelationship among them however it is overlooked
in the travel behavior analysis especially in the aforementioned studies.
2. The effects of mobile phone and telecommuting are analyzed according to tour
number and tour complexity.
3. The integration of the social dimension like social activities, social network and
social interaction still do not exist in the travel behavior analysis. It is therefore
the aim of this study to execute some of the missing social aspects realized in the
existing transportation studies reviewed. The succeeding chapters present the
empirical studies on the key issues mentioned above in modeling and analyzing
the impacts of ICT and social dimension on travel. While studies on ICT and
56
social dimension exist and gradually have gain attention in the filed of
transportation studies, yet, there are still remaining areas of social dimension that
needs to be thoroughly examined. For example, social interaction is scarcely
dealt and is overlooked in the existing studies even though ICT applications
nowadays are immensely and preferably used for social interaction purposes. As
a matter of fact, 89% of the Americans connect to internet in order to interact and
keep up with their friends (PEW, 2008).
4. In addition, the concept of ICT use, social dimension and time planning are
analyzed since they are rarely examined based from the previous studies reviewed.
5. It is found out that most of studies on ICT are hailed from the developed
countries however extremely limited studies from the developing countries. For
this reason, it is still vague to make a statement that the impact of ICT in the
developed countries is holds true in the developing countries.
6. Last but not the least important, the classification of telecommuting employed in
this research is according to the amount of time of use, which was not carried out
in the previous studies.
For the time being, these are the key issues dealt in this research. Other key issues that are
related, however, not dealt in this study are left as remainders and are subject for
recommendations for future works.
***
57
CHAPTER 3 INFORMATION AND COMMUNICATIONS TECHNOLOGY ADOPTION
AND TRIPS
3.1 Introduction
Through mobile phones, people can get connected to their family, friends and colleagues
almost everywhere and anytime. Household members might call during a journey to ask for
a favor for an additional errand that on certain occasions obliges the traveler to make another
trip. There are times that friends might call on the mobile phone while on the trip to arrange
a short meeting, dinner or a joint activity which could change the usual trip pattern. In
summary, mobile phones are often used for short notice coordination and organization of
schedules for various purposes (Pica and Kakihara, 2003). However, there are also instances
that trips can be avoided by using mobile phones. For example, a sudden change or
cancellation of a business meeting can be arranged even if the person is not in the office or at
home. Therefore, the possession of mobile phones could either eliminate trips or it could
lead to more trips.
Work trips might be influenced by information technologies in further ways. Increasingly,
work can be done at home without any hassle of commuting everyday for work due to ICT,
which has been prematurely identified as the cause of the “death of distance” (The Economist,
1997). Telecommuting is generally defined as working at home or at an alternate location
and communicating with the usual place of work using electronic or other means instead of
physically traveling to a more distant work site (Mokhtarian, 1991). This implies that those
who adopt telecommuting might reduce their daily work trips. But it might be that their
reduced work trips are replaced by an increase in other trips, such as leisure or shopping trips.
Since telecommuting reduces commuting time, people who adopt it might have more time for
household chores or family errands.
58
The main objective of this study is to explore the effect of information and communications
technology (ICT) on the daily weekday trips. In particular, this research focuses on the
effects of mobile phone possession on the frequency of daily trips and the effects of
telecommuting on total trips. In contrast to previous contributions, this study also seeks to
understand the amount of time of telecommuting needed to cause a shift in travel patterns.
Note that in this study telecommuting is defined as using a computer at home for work, i.e.
not working from other non-home and non-work places.
The rest of the paper is arranged as follows. Section 3.2 reviews related research regarding
the effects of mobile phone and telecommuting on travel behavior and proposes our
hypotheses on the impacts of mobile phone possession and telecommuting on travel behavior.
Section 3.3 presents the overview of London and it describes the data used in the analysis and
presents the result of the descriptive analysis. Section 3.4 exemplifies the empirical
regression results and discusses the effects on trips. Section 3.5 summarizes the results of
this paper and discusses implications.
3.2 Literature review
Our literature review is further subdivided as follows. After a short general discussion on ICT
effects on travel behaviour, we review the complementary and substitutive effects of mobile
phone followed by a review on the effects of telecommuting on travel. Based on these
findings Section 3.2.2 then develops hypotheses that we aim to confirm and extend with our
London data.
3.2.1 Previous studies
Generally, ICT provides people alternatives to face-to-face communication and thus has a
potential to substitute physical travel. Wang and Law (2007) define ICT use as utilizing
email, internet, video conferencing or video phone for either business or personal purposes.
Using the structural equation model, their study suggests that the use of ICT triggers
59
additional time use for out-of-home recreational activities and tends to increase the frequency
of trips.
In addition, Hjorthol (2008) conducted a survey investigating the relationship between mobile
phone use, planning of everyday activities and car usage in families with children. Her
results suggest that aside from the significantly positive relationship between car use and the
use of mobile phone, short planning is also positively related to mobile phone use. In
addition, Viswanathan and Goulias (2001) investigate the effects of both mobile technology
and internet use on travel times and find that mobile technology and travel times are
complementary whereas internet use and travel times are substitutive. Bhat et al. (2003)
study the impact of ICT, particularly of mobile phone adoption, on non-maintenance
shopping activity. According to their result, however, the substitution between mobile
phone use and shopping travel exists and is underestimated when the effects of common
unobserved attributes that affect mobile phone adoption and shopping travel are not
considered. Alexander et al. (2009) conduct a study in the regions of Utrecht, Amersfoort
and Hilversum examining the causal relationship between ICT and fragmentation of
paid-work trips. The empirical results of their study show that mobile phone (and even
landline phone) possession are highly associated with the temporal as well as the spatial
fragmentation of paid work, which increases the number of work-related trips and the time
spent on travel. Some studies with aggregate data (e.g., Choo et al. 2007, Choo and
Mokhtarian 2005) also support the hypothesis that travel and telecommunication have a
complementary relationship.
Further, telecommuting allows people to keep away from the hassles of commuting by
reducing physical trips. Therefore, telecommuting is often suggested to be one of a series of
policy measures to reduce travel demand (e.g., Nilles, 1974; Kraemer (1982); Mokhtarian and
Salomon, 1997). Telecommuting instead of actual commuting might, however, often reduce
travel demand less than hoped for by transport planners. Using time-series data from the
national statistics office in Canada, Norway and Sweden, Harvey and Taylor (2000) reveal
that working in isolation at home does not really diminish travel. Especially if
telecommuting from home, some people may get bored of their environment and rather spend
more time to shop, to do household chores or to socialize with friends. Furthermore, Douma
et al. (2004) conduct a study in Minneapolis/St.Paul which focused on work and shopping
behavior at household level. Their study reveals that e-workers take advantage of using ICT
60
to modify their travel patterns without impacting their workday. Instead, ICT is used before
or after work to maintain contact with their office while leaving for or from work at times.
Likewise, Tilahun and Levinson (2010) mention that organizing or scheduling social
meetings is constrained by time and location (home and work). Telecommuting and having
a flexible work schedule helps loosen these constraints. Furthermore, Mokhtarian and
Salomon (2002) study the effects of working from (nearby) telecommuting centers on macro
and micro-scale level. They point out that also this kind of telecommuting may change land
use patterns due to changes in travel patterns. Compared to commuting to the (farther away)
company office, they find center-based telecommuting to cause a small increase in commute
trips on telecommuting days, mostly due to trips home for lunch and back to the center in the
afternoon. This conforms to the study of Balepur et al. (1998) who examine the impacts of
center-based telecommuting. Their result indicates that on telecommuting days the number
of return home, eating out, shopping, and social/recreational trips is higher. Finally, the
hypothesis of substitution between travel and ICT is supported by Srinivasan and Athuru
(2002) using activity-diary data from the San Francisco Bay Area. Their study focuses on
the relationship between physical and virtual activity participation in maintenance and
discretionary activities.
3.2.2 Hypotheses
This study contributes to the growing literature on ICT and travel behavior by analyzing a
large sample of London residents. In contrast to previous studies, this study investigates the
effect of having mobile phone and telecommuting not only on trips. We consider trips as the
dependent variable. Based on previous literature, hypothesis is initially formulated to
identify its effects on trip frequency. Further, hypothesis is established with regards from
the effect of mobile phone possession and telecommuting.
A. Trip Frequency
A.1. The number of trips per day is hypothesized to be positively associated with
mobile phone possession. Our rationale is that the trip generating effects of mobile
phone possession seem to outweigh the trip reducing effects in previous literature
(e.g., Bhat et al 2003).
61
A.2. It is reasonable to assume that work trips are reduced through telecommuting,
though for example Douma et al. (2004) show that using ICT does not necessarily
induce a significant change in work patterns.
(a)
(b)
Figure 3.1 Illustration of hypotheses (a) shows the hypothesis of the effect of mobile phone possession on trips as stated in A.1 (b) represents the hypothesis of the effect of telecommuting on trips as discussed in A.2, A.3, A.4.
62
A.3. We further hypothesize that non-work trips of telecommuters increase as found
by for example Harvey and Taylor (2000). When people reduce their work trips, they
will have more freedom for leisure or shopping activities.
A.4. Total trip numbers are hypothesized to be unchanged or slightly increase through
telecommuting as suggested by Balepur et al. (1998).
These hypotheses are illustrated in Figure 3.1. Both telecommuting and mobile phone
possession might lead to more trips. The effects of telecommuting and mobile phone are
also tested with regards to tour numbers which will be discussed in the succeeding chapter.
3.3 Data structure and descriptive Analysis
3.3.1 Overview of London
London is the capital of England and the United Kingdom (UK) - a developed country. It is
the most heavily populated metropolitan area in UK and largest urban zone in the European
Union (Eurostat, 2006). As illustrated in Figure 3.1, London has escalating population
Figure 3.2 Population densities in London (1996-2008)
4469.18
4637.37
4771.79
4300
4350
4400
4450
4500
4550
4600
4650
4700
4750
4800population density
Year (1996-2008)
Pop
ulat
ion
per
squa
re k
ilom
eter
63
density from 1996 to 2008, with 4,469.18 populations per square kilometer in 1996 that
turned out to be 4,771.9 in 2008. According to the Office of National Statistics UK (2007),
London has approximately a total population of 7.5 million in 2007 with 4.1 million belong
to the working population. Of the working population, 2.1million are male workers and 2
million are female workers, as shown in Figure 3.3. For this research, only the working
population in London is investigated together with their use of ICT.
London is also a major center for international business and commerce. Consequently,
London is considered as one of the top three “command center” for the world economy,
together with New York City and Tokyo. As shown in Figure 3.3, the three basic indicators
of ICT as well as the per capita are depicted for the entire UK. Mobile phone subscription
has a dramatic increase from the year it commenced. In 2001, UK has roughly 45 million
mobile subscribers, which approximately corresponds to the number of the mobile
subscribers in Metro Manila between 2006 and 2007, about 43 million (ITU, 2008). As it
continuously growing in number of subscriptions, it significantly overtakes landline
subscription in year 2000. Along with the abrupt shift of mobile phone subscriptions is also
Figure 3.3 Working population by gender in London (year 2007)
2108000 (52%)
1971000 (48%) male
female
Source: [email protected]
64
Figure 3.4 Mobile phone, Landline, Internet subscription
the increasing pattern of per capita. The subscription of internet also increases though not as
rapid as the mobile phone while landline subscriptions remain horizontal starting year 2000
with a little striking decrease in 2008.
The data of these indicators, specifically, for London alone is scarcely available. This study
utilizes ICT data integrated in the survey performed by the Transport for London (TfL) in
2001.
Table 3.1 presents the car ownership in each household in London with comparison between
1991 and 2001. Majority of the daily journeys in Central London are made by public
transport, although car travel is also most common in suburbs. This is evident in the table
where more cars for households living in the Outer London than in the Inner London. In
Source: ITU, 2008
Year (1996 - 2008)
Fre
quen
cy
65
Table 3.1 Household car ownership by area of residence
Percentage of households
Number of household cars Inner London Outer London All London
1991 2001 1991 2001 1991 2001
No cars 54 51 32 28 41 37
One car 36 39 45 46 41 43
Two cars 9 9 19 21 15 16
Three or more cars 1 1 4 5 3 4
All households 100 100 100 100 100 100
Note: Inner London results here consists of Central and Inner London combined
Source: Transport for London (2001)
2001, fewer households have no cars (37%) compared to 1991 (41%). In contrast,
households with one car have increased from 41% in 1991 to 43% in 2001. There are about
20% households in 2001 have two or more cars compared to 18% in 1991.
3.3.2 Data description
The data used for our analysis are extracted from the London Area Travel Survey (LATS)
2001 data, made available by TfL. The survey collected information on the regular
weekday travels of people living in Greater London. All interviews were done on a personal
basis, and respondents were asked to fill in a 1-day travel survey. In total, 67,252
individuals from 29,973 households were interviewed which corresponds to a response rate
of about 1%. The survey results are made available in four main data tables. Firstly,
household information; secondly, information about the individual; thirdly, trips made by the
individual and fourthly information about the vehicles owned by the household. From the
first and second tables, we extract socio-demographic information, in particular information
66
whether the respondents’ possess a mobile phone, his working status and how many hours per
week the respondent is using his PC to work from home. Unfortunately, this data set does
not have any information on how much a person is using his/her mobile phone. Bearing in
mind our objectives, we opt to exclude all non-working respondents which leaves us a with a
sample size of 27,634 individuals who made a total of 87,148 trips on the day they were
interviewed. The trip information includes the modes chosen, the trip activity duration as
well as the type of activities which were carried out at the destination. Note that during
2001, when the survey was conducted, mobile phone possession was still likely to be
correlated with income and hence working trips. This is a second reason to focus our
analysis on the working population. Further, our following analysis in particular controls
for income and distinguishes effects of ICT on total trips as well as different trip.
3.3.3 Descriptive analysis of mobile phone impact
As shown in Table 3.2, approximately 44% of the overall respondents in the sample state that
they possess a mobile phone. By comparing this to the statistics of the Office of
Telecommunications, UK (OFTEL, 2004), we find a significant difference. To identify the
reasons, additional information is presented in Table 3.3 from other agencies that collected
information on mobile phone penetration. EUROSTAT data are based on subscriptions or
sales data, OFTEL, Office of National Statistics (ONS) and LATS are based on individual
surveys. ONS and LATS mobile penetration rates are fairly similar, whereas the rates given in
EUROSTAT and OFTEL appear significantly different. OFTEL data are, however, only partly
compatible as these are data on “possesses or uses” a mobile phone. Note also that in OFTEL
the percentage of those using their mobile phone as main mode of telephony is significantly
lower (15%). Both LATS and ONS rates are based on surveys interviewing individuals. We,
therefore, suspect that the difference in statistics is partly due to differences between sales
based and individual based statistics of mobile phone possession. Sales based data might
overestimate possession of actively used mobile phones due to multiple ownership of phones,
whereas individual based data might underestimate possession of mobile phones due to
omitting to report the possession of mobiles that are seldom used.
Hence, it presume that respondents who use mobile phones as their primary phone connection
might have answered affirmative to the reviewer’s question on mobile possession.
67
Respondents who just occasionally use their mobile phone might have answered with “no” in
order to avoid being asked for their mobile phone number. A “yes” answer for the previous
question on landline possession is followed by a question if the respondents is willing to
provide his/her number. In conclusion, though we keep our term mobile phone owner in line
with the survey question, those who affirmed having a mobile phone, might be more
accurately referred to as “heavy” user and those who answered negative might be more
appropriately called “occasional or not” mobile phone user.
Table 3.2 Mobile phone and personal computer information
Frequency Percent
Mobile phone possession
Have 12,144 43.95
Don’t have 15,490 56.05
Personal computer possession
Have 18,520 67.02
Don’t have 9,114 32.98
Work type and Telecommuting
Full time working, do not use PC for work (not) 17,095 61.86
Full time working, uses PC for work 1-9 hours per week (some) 4,655 16.85
Full time working, uses PC for work ≥10 hours per week (much) 1,147 4.15
Part time working, do not use PC for work (not) 3,773 13.65
Part time working, uses PC for work 1-3 hours per week (some) 609 2.20
Part time working, uses PC for work ≥4 hours per week (much) 354 1.28
Table 3.3 Mobile phone penetration rate by agencies
EUROSTAT 2001
OFTEL 2001 ONS –UK 2000-01
LATS 2001 SAMPLE (Sample
size: 27,634)
LATS 2001 ALL (Sample size:
53,020) 76 67* 47 44 35
Note: * = own or use, 15% uses mobile phone as the main mode of telephony.
68
Table 3.4 Mobile phone penetration rate by Age
Age group OFTEL 2001 LATS 2001
SAMPLE LATS 2001 ALL Difference
between OFTEL and LATS 2001 SAMPLE
15-24 83 48 40# 35 # 25-34 84 48 44 36 35-44 78 45 42 33 45-54 70 41 37 29 55-64 59 36 29 23 65-74 41 29 16 12 75 and over 13 21 8 -8
Note: #
= age 16-24
Table 3.5 Mobile phone penetration rate by Income
Income bracket ONS-UK 2000-01 LATS 2001 SAMPLE Top fifth 66 52 Next fifth 60 49 Middle fifth 52 43 Next fifth 34 40 Bottom fifth 23 36
Table 3.6 Penetration rate by Employment type (LATS 2001 Sample)
Employment Type (Sample size) Penetration rate White collar (4503) 49.25 Admin (2971) 40.98 Health (3071) 43.65 Blue collar (4464) 39.81 Transport related (494) 44.62 Self employed (41) 32.79
Tables 3.3 to 3.6 discuss some socio-demographic characteristics of mobile phone users in
our LATS sample. Firstly, we note that the extracted working population sample has a slightly
higher penetration than the total LATS sample (a total of 53020 respondents that includes the
unemployed). This is, however, expected due to income effects on mobile ownership as
shown in Table 3.4. The difference compared to all LATS as well as OFTEL data is fairly
69
constant among younger age groups though decreasing for those near retirement. One might
speculate that this is because middle aged and older persons less frequently omit the reporting
of their mobile. (Though especially for the 75+ our sample size is, as expected, very small
(with 61 out of 27634 aged 75+).
Table 3.5 further illustrates that the difference in penetration rate between ONS-UK data
(2001) and the LATS 2001 sample differs between income groups. Whereas LATS data report
lower ownership rates for high income groups, ownership in lower income groups is higher.
The reasons for this are not fully understood. One might argue that this is partly a London
effect where, among those being employed, income might not be as strong a determinant for
mobile ownership as in other parts of the UK with on average lower incomes. Table 3.6
groups ownership by those employment types also subsequently distinguished in this paper.
Those with blue collar jobs have lower ownership rates as one would expect according to
their income. Our sample of self-employed is too low to conclude that the difference is
significant.
Finally, note that in general we would expect to see higher mobile phone ownership rates in
our sample compared to the other, whole UK based, data sources used in this section. As
discussed, ownership is related to employment and income which is higher in London than in
other part of the UK. Further factors likely to favor higher ownership rates in London are
network availability, more dispersed travel patterns and family structures. It should be further
kept in mind that the surveys were carried out in 2001, when mobile phone usage was fast
increasing.
Figure 3.5 illustrates that those in possession of a mobile phone make slightly more trips than
those without a mobile phone (3.522 compared to 3.424 trips per day). The average number
of trips for each trip purpose might have small differences between those with mobile phone
and those without. The unpaired t-test analysis confirms that this difference is statistically
significant (N = 27634, t =4.58, p < 0.001); however, one should possibly be slightly cautious
with this and the following t-test results, as our large sample size of two independent samples
will easily lead to significant t-values. Work trips are higher for those having a mobile phone
(N= 27634, t = 5.10, p < 0.001) but also a small increase can be seen for leisure and personal
business trips. Especially, for the relationship between work trips and mobile phone
70
Figure 3.5 Effects of mobile phone possession on trip frequency (for each type of trip)
possession, though, the causal relationship between the two is not clear as argued above.
Though there might be a similar mixed causal relationship also for leisure and shopping trips,
it is probably more likely to assume that mobile phone affects these trip numbers than vice
versa. Therefore the significant increase (N = 27634, t = 3.75, p < 0.001) in leisure trips
suggests that mobile phone possession might be associated with additional activities as
hypothesized in A.1. Shopping trips exhibit no significant difference. However, in order to
separate income, age and effects of mobile phone possession, a regression analysis is
performed and described in Section 3.4.
3.3.4 Descriptive analysis of the impact of using home PC for work
From Table 3.2, it can be seen that approximately 67% of the respondents have a personal
computer at home. According to how many hours per week respondents use their PC to work
2.155 2.230
0.320 0.3500.281 0.2860.430 0.4110.226 0.235
0.000
0.500
1.000
1.500
2.000
2.500
3.000
3.500
4.000
no mobile phone with mobile phone
drop off/pick up
holiday home
education
personal business
shopping
leisure
work
Aver
a ge
num
ber o
f trip
s per
day
Mobile phone possession
71
from home, we further classify respondents as much, some or not telecommuting. For full
time working people, we define those using their PC for work from home as more than 1 full
working day (≥10 hours) as much telecommuting. “Some telecommuting” (1-9 hours)
might hence also be employees or employers who usually work from the office but take some
remaining work home. For part time working people, we set our threshold to ≥4 hours to
reflect the overall reduced working time.
As shown in Table 3.2, those who work full time but do not telecommute comprise of about
62% of the total respondents. Approximately 17% are full time workers who do some
telecommuting and only 4 % are full time workers who telecommute much. Almost 17% of
our sample are part time workers. Out of these, 21% do at least some of their PC work from
home.
As illustrated in Figure 3.7, among those who telecommute, generally the more a person is
using his PC to work from home the less trips per day he/she does. The average trip number
Figure 3.6 Average number of trips and the duration of
personal computer use to work from home
0.000
0.500
1.000
1.500
2.000
2.500
3.000
3.500
4.000
0 1-3 4-9 10-14 15-19 20-29 30-34 35-39 40-49 >50
All trips
Full time work trips
Part time work trips
Usage of personal computers for work (number of hours per week)
Aver
a ge
num
ber o
f trip
s per
day
72
per day for those not telecommuting (at all) from home is similar to those using their
computer 35-50h per week. As argued before, the increase in total trips comparing “no” or
at least “1 hour” using PC from home is likely due to work trips. A second possible
explanation is that this might be due to those performing jobs that demand more work trips
such as business trips or visiting customers. These respondents would also be more likely to
use their computer at home at least sometimes in the evening, for example to check emails
and make a schedule for the following work day.
The more a person is working from home, the more work trips are reduced as one would
expect. However, comparing this to total trips we can see that the non-work trips are
increasing, suggesting that the freedom gained through working from home will be used for
additional activities. This is further investigated with the cross tabulation of average trips
per day by trip purpose and by work/telecommuting status in Table 2. The trip destination
purposes are divided into 7 groups: (1) work, (2) shopping, (3) leisure, (4) personal business,
(5) education, (6) holiday home and (7) drop off or pick up. Moreover, the work and
telecommuting status is classified into the six groups, as also presented.
With Table 3.7, it can be identified which trip purposes increase and which decrease
depending on the work status. Using the analysis of variance (ANOVA) among the three
groups of full time working respondents, we find that there is a statistically significant
Table 3.7 Average number of trips per day by destination and by work type
Work type and
Telecommuting status
Destination purpose Total
Work Shop- Ping Leisure
personal business
Educa- tion
holiday home
drop off/ pick up
Full time working, not 2.170 0.301 0.236 0.417 0.008 0.001 0.159 3.293 Full time working, some 2.366 0.395 0.269 0.411 0.009 0.002 0.210 3.663 Full time working, much 1.997 0.371 0.335 0.446 0.007 0.000 0.243 3.400 Part time working, not 2.131 0.349 0.436 0.446 0.026 0.001 0.482 3.870 Part time working, some 2.248 0.478 0.502 0.417 0.025 0.002 0.634 4.305 Part time working, much 1.822 0.494 0.531 0.427 0.042 0.000 0.480 3.797
73
difference between these three groups (F = 13795.61, d.f. = 2). Those who are not
telecommuting do the least total trips but those who do some telecommuting do in fact more
work trips than those who telecommute much, possibly because of the job type effects
previously described. Further, our analysis confirms that there is a complementary effect
towards more leisure trips when doing much telecommuting. The more a person works
from home, the more freedom it appears he/she has to perform additional leisure. There is
also an increase in personal business trips when doing much telecommuting but it does not
appear to be statistically significant.
In the same way, ANOVA test is performed among the part time working sample (F =
2506.19, d.f. = 2). The result indicates that those who are doing some telecommuting make
most work trips, followed by those not doing telecommuting at all, with those who do much
telecommuting doing least work trips. We suspect again that the reason for the significant
increase of work trips for those who do some in telecommuting might be due to the nature of
their work, which requires them to use their PC at home for work but not necessarily reduces
the need to make a trip for work. Hence, we control for work type and their telecommuting
status in our regression analysis. The trends described for the other trip purposes follow the
trends described for full time workers, however, on a generally higher level of average trips
per day. The significantly higher drop off/pick up trips further support our expectation that
it is in general the part time working parent who would take over these responsibilities.
Both full time and part time workers, regardless of their telecommuting status, make similar
numbers of personal business trips.
3.4 Regression analysis
3.4.1 Model specification
The ordered probit regression is most suitable for modeling with a dependent variable that
takes more than two values, where these values have a natural ordering. In contrast to a
linear regression model, it does not assume cardinality. We further consider count data
analysis (e.g., Jang, 2005) but it appears not suitable for this case due the distribution of our
74
dependent variable. In the ordered probit model, the dependent variable is latent (i.e.,
unobserved variables) and expressed as:
𝑦𝑖∗ = 𝒙𝑖𝜷 + 𝜀𝑖, (eqn. 3.1)
where 𝑦𝑖∗ is a latent variable measuring the number of daily trips for individual i (i = 1,..., N)
and N is the sample size; 𝒙𝑖 is a (k × 1) vector of independent (observed) nonrandom
explanatory variables; 𝜷 is a (𝑘 ×1) vector of unknown (coefficients) parameters; 𝜀𝑖 is the
random error term, which is assumed to be normally distributed with zero mean and unit
variance.
Let 𝑦𝑖 denote the number of observed trips per day. To convert the continuous latent variable
𝑦𝑖∗ into the discrete observed number of trips, a set of 𝝁 (n× 1) is introduced where n
the number of trip categories as shown below:
𝑦𝑖 =
⎩⎪⎪⎪⎨
⎪⎪⎪⎧
0 if − ∞ ≤ 𝑦𝑖∗ ≤ 𝜇1
1 if 𝜇1 ≤ 𝑦𝑖∗ ≤ 𝜇2
2 if 𝜇2 ≤ 𝑦𝑖∗ ≤ 𝜇3…
𝑛 + 1 if 𝜇𝑛 ≤ 𝑦𝑖∗ ≤ ∞,
� (eqn. 3.2)
where the vector of threshold values 𝝁 are unknown parameters to be estimated along with
the parameter vector 𝜷. In subsection 3.4.3, we specify different models of the number of
daily trips for all trips, work trips only, leisure trips only, those making at least one trip.
The parameters are to be estimated so that yi* is expected to change by 𝛽𝑘 for a unit change
in xik
𝑃𝑟(𝑦𝑖 = 𝑚|𝒙𝑖) = 𝐹(𝜇𝑚 − 𝒙𝑖𝜷) − 𝐹(𝜇𝑚−1 − 𝒙𝑖𝜷), (eqn. 3.3)
, holding all other variables constant. The maximum likelihood method is employed to
estimate the parameters of the model (Long, 1997). The predicted probability of the number
of trips (stops) 𝑚 for given 𝒙𝑖 is
where 𝐹 is the normal cumulative distribution function.
75
The log likelihood function is the sum of the individual log probabilities as follows
𝐿𝐿 = ∑ ∑ 𝑍𝑖𝑗log�𝐹�𝜇𝑗 − 𝒙𝑖𝜷� − 𝐹(𝜇𝑗−1 − 𝒙𝑖𝜷)�𝑛𝑗=0
𝑁𝑖=1 , (eqn. 3.4)
where Zij is an indicator variable which equals 1 if yi
= j and 0 for otherwise.
3.4.2 Control variables in regression model
The percentage of the various social-demographic control variables used in this study is
tabulated in Table 3.8 as a separate column for each of the four specified models. After
various model testing, we group our respondents into seven age categories. Following
previous studies with the LATS data on trip frequency of older Londoners by Schmöcker et al.
(2005), ethnicity is included and grouped as white (almost 80%) and non-white while a more
detailed classification was found to be insignificant. Further several household types are
distinguished. 20% of the respondents are living alone and 5% are single parents with
dependent children. About 35% of the respondents live with a spouse or partner and
approximately 29% are married with dependent children. Note that nearly 1% of our
respondents state that they are living in “all pensioner” household. These are presumably
older respondents who still have some (part-time) jobs or are still involved in some way in
their former work place. Among the respondents, nearly 80% have a car license. We
further include car ownership as a continuous variable in the model. 78.83% of Londoners
own a car, but since a number of households own multiple cars, the average is 1.12 cars per
household. As work type and income are correlated these two are interacted by
distinguishing white collar jobs, admin/clerical jobs, health related jobs, blue collar jobs,
transport related jobs and being self-employed.
To further control for geographic characteristics, population density data obtained from
Census data are matched with the first three-digits of the respondents’ home-address post
code, available from the LATS data. Tests defining population density as a continuous or
categorical variable, suggest a better fit for the latter. We define 5 categories with 4% of the
sample living in the least densely populated areas (4000 per mi2 and below) and 17% residing
in the most densely populated parts of London (over 25,000 per mi2). As areas with low and
high population density can be found in both Inner and Outer London, we further include this
76
Tabl
e 3.
8 O
rder
ed p
robi
t mod
els
for
num
ber
of w
eekd
ay tr
ips
M
odel
1
Mod
el 2
M
odel
3
Mod
el 4
All
trip
s W
ork
trip
s L
eisu
re +
Sho
ppin
g A
ll tr
ips
filte
red
resp
onde
nts
Per
cent
(%
) E
stim
ate
t-st
at P
erce
nt
(%)
Est
imat
e t-
stat
P
erce
nt
(%)
Est
imat
e t-
stat
P
erce
nt
(%)
Est
imat
e t-
stat
Cut
poi
nts
(A
ll tr
ips,
Wor
k tr
ip, L
eisu
re tr
ip)
0 tr
ips
26
.05
0.08
2 1.
352
59.9
7 0.
592
9.81
2
1 tr
ips
9.99
-0
.904
-1
5.65
8 57
.29
1.75
2 30
.045
25
.70
1.43
4 23
.609
1.
91
-1.9
29
-28.
265
2 tr
ips
43.0
6 -0
.797
-1
3.81
6 16
.66
---
---
14.3
3 --
- --
- 46
.93
0.17
7 3.
198
3 tr
ips
9.29
0.
592
10.2
88
51.1
7 --
- --
-
4
+ tr
ips
37.6
6 --
- --
-
Soc
io-d
emog
raph
ic
Gen
der
M
ale
=1
(fem
ale
=0)
54
.55
-0.0
93
-6.0
65
0.
081
5.36
5
-0.1
19
-7.4
48
54.4
9 -0
.126
-7
.448
A
ge
Age
16-
24 (
refe
renc
e)
8.29
--
- --
-
---
---
--
- --
- 8.
23
---
---
Age
25-
34
28.9
8 0.
050
1.77
8
-0.0
18
-0.6
27
-0
.014
-0
.461
28
.85
0.04
2 1.
330
Age
35-
44
29.4
2 0.
083
2.89
8
-0.0
21
-0.7
39
-0
.032
-1
.053
29
.54
0.06
2 1.
941
Age
45-
54
20.5
8 0.
023
0.79
3
-0.0
45
-1.5
21
-0
.071
-2
.268
20
.63
-0.0
14
-0.4
16
Age
55-
64
10.8
7 0.
065
1.98
0
-0.0
25
-0.7
68
-0
.091
-2
.595
10
.85
0.03
8 1.
039
Age
65-
74
1.86
0.
262
4.08
1
-0.0
68
-1.0
86
0.
207
3.21
7 1.
90
0.21
0 2.
983
Age
75
and
abov
e 0.
22
0.30
2 1.
900
-0
.148
-0
.943
0.08
0 0.
513
0.23
0.
285
1.64
7 R
ace
W
hite
= 1
(N
on-w
hite
= 0
) 77
.63
0.20
2 11
.724
0.11
7 6.
812
0.
276
14.6
58
78.0
1 0.
202
10.5
10
Car
lice
nse
W
ith li
cens
e =
1 (
No
licen
se =
0)
79.6
5 0.
180
9.18
2
0.04
8 2.
472
0.
135
6.45
1 80
.01
0.14
5 6.
604
Car
ow
ners
hip
1.12
#
0.00
4 0.
349
-0
.003
-0
.208
-0.0
29
-2.2
03
1.12
-0
.036
-2
.599
H
ouse
hold
str
uctu
re
S
ingl
e
16.6
4 -0
.049
-2
.036
0.06
5 2.
744
0.
287
11.5
17
16.5
7 -0
.032
-1
.223
S
ingl
e pa
rent
with
dep
ende
nt c
hild
ren
5.15
0.
167
4.73
4
0.06
9 2.
016
0.
172
4.78
2 5.
28
0.13
5 3.
502
Mar
ried
/co-
habi
ting
34
.57
-0.3
09
-8.6
65
0.
036
1.92
4
0.06
3 3.
206
34.5
2 -0
.161
-7
.719
77
[Tab
le 3
, con
tinue
d]
Mar
ried
with
dep
ende
nt c
hild
ren
(ref
eren
ce)
28.
54
---
---
--
- --
-
---
---
28.8
8 --
- --
- A
ll pe
nsio
ners
0.
93
-0.1
99
-3.6
06
0.
032
0.37
1
0.00
5 0.
060
0.95
-0
.426
-4
.550
[T
able
3, c
ontin
ued]
All
othe
r ho
useh
olds
14
.17
0.20
2 -8
.420
0.01
3 0.
553
0.
092
3.68
3 13
.80
-0.1
47
-5.5
72
In
tera
ctio
n be
twee
n ho
useh
old
inco
me
and
empl
oym
ent
typ
e
# H
ouse
hold
inco
me
* W
hite
col
lar
job
4453
5.67
0.
042
9.94
6
0.03
9 9.
507
0.
048
11.0
79
4471
0.01
0.
049
10.6
16
Hou
seho
ld in
com
e *
Adm
inis
trat
ive
job
35
828.
11
0.04
5 8.
254
0.
039
7.38
0
0.05
7 10
.292
35
828.
41
0.06
3 10
.207
H
ouse
hold
inco
me
* H
ealth
rel
ated
jo
b 37
456.
42
0.04
1 8.
007
0.
004
0.83
8
0.06
7 12
.932
37
565.
18
0.05
8 10
.152
H
ouse
hold
inco
me
* B
lue
colla
r jo
b 28
438.
85
0.01
8 3.
127
-0
.005
-0
.842
0.03
9 6.
542
2829
8.09
0.
032
4.99
3 H
ouse
hold
inco
me
* S
elf-
empl
oyed
32
991.
80
-0.1
44
-3.9
80
-0
.103
-2
.727
-0.0
39
-0.8
83
3184
5.24
-0
.037
-0
.776
H
ouse
hold
inco
me
* T
rans
port
-rel
ated
job
2874
1.59
0.
039
3.14
0
-0.0
37
-3.0
53
-0
.001
-0
.051
29
252.
19
0.05
3 3.
922
Pub
lic tr
ansp
ort a
nd T
rip
Des
tinat
ion
Pub
lic tr
ansp
ort u
ser
= 1
(no
n-us
er =
0)
32
.65
-0.3
46
-18.
382
At
leas
t on
e tr
ip w
ith d
estin
atio
n w
ithin
Cen
tral
L
ondo
n =
1 (o
ther
wis
e 0)
18.7
7 0.
085
3.97
7
Geo
grap
hic
char
acte
rist
ics
A
rea
In
ner
Lon
don
=1
(Out
er L
ondo
n =
0)
34.8
3 -0
.063
-3
.245
-0.0
17
-0.8
77
-0
.059
-2
.896
34
.38
-0.0
46
-2.1
18
Pop
ulat
ion
dens
ity
(pop
ulat
ion/
squ
are
mile
)
10
00-2
000
2.
19
-0.0
33
-0.6
10
-0
.053
-1
.000
-0.0
84
-1.5
21
2.17
-0
.027
-0
.444
20
00-4
000
2.00
0.
028
0.50
2
0.04
5 0.
827
-0
.114
-1
.971
2.
06
-0.0
63
-1.0
25
4000
-100
00
22.7
3 0.
002
0.05
3
0.03
3 1.
148
-0
.077
-2
.555
22
.93
-0.0
28
-0.8
66
1000
0-25
000
56.4
3 -0
.028
-1
.230
-0.0
10
-0.4
23
-0
.083
-3
.450
56
.51
-0.0
57
-2.2
20
Ove
r 25
000
16.6
5 --
- --
-
---
---
--
- --
-
---
---
78
[Tab
le 3
, con
tinue
d]
Mob
ile P
hone
Pos
sess
ion
mob
ile p
hone
(w
ith m
obile
pho
ne =
1)
43.9
5 0.
032
2.24
4
0.01
8 1.
262
0.
017
1.17
0 44
.26
0.01
8 1.
112
Tele
com
mut
ing
stat
us
Full
tim
e w
orki
ng,
do
not
use
PC
for
wor
k 61
.86
0.13
7 3.
865
0.
596
16.5
77
-0
.141
-3
.852
62
.05
0.00
6 0.
152
Full
tim
e w
orki
ng,
us
es P
C f
or w
ork
1-9
hour
s pe
r w
eek
16.8
5 0.
240
6.35
7
0.63
6 16
.703
-0.0
37
-0.9
55
17.0
4 0.
154
3.57
7 Fu
ll ti
me
wor
king
,
uses
PC
for
wor
k ≥
10 h
ours
per
wee
k 4.
15
---
---
--
- --
-
---
---
3.92
--
- --
- P
art t
ime
wor
king
,
do n
ot u
se P
C f
or w
ork
13.6
5 0.
322
7.91
3
0.13
6 3.
320
0.
243
5.13
5 13
.59
0.26
6 5.
745
Par
t tim
e w
orki
ng,
us
es P
C f
or w
ork
1-3
hour
s pe
r w
eek
2.20
0.
453
7.49
6
0.02
0 0.
344
0.
313
5.35
0 2.
21
0.44
6 6.
512
Par
t tim
e w
orki
ng,
us
es P
C f
or w
ork
≥ 4
hour
s per
wee
k 1.
28
0.19
8 2.
792
-0
.616
-8
.146
0.31
5 4.
487
1.18
0.
387
4.55
9
N
umbe
r of
obs
erva
tion
s 27
634
2763
4 27
634
2535
7 L
og L
ikel
ihoo
d In
terc
ept o
nly
4896
8.65
47
456.
63
4570
7.55
36
616.
44
Log
Lik
elih
ood
Fina
l 47
907.
27
4573
5.15
44
396.
61
3515
9.43
M
cFad
den
R0.
02
2 0.
03
0.03
0.
04
#
h
ave
an a
vera
ge v
alue
inst
ead
of p
erce
nt
79
variable as a separate dummy variable. About 35% of our respondents live in Outer
London.
Finally, among those who make at least one trip per day, we include two further dummy
variables. As car usage tends to increase trip frequency we include as a dummy variable
whether the respondent has used public transport or not. Around 1/3 of the travelers within
our sample have used public transport at least once. Further, we include a control variable
on destination of trip. We distinguish those who have travelled at least once into Central
London (what is since 2003 the Congestion Charging zone). Our reasoning is that trip
patterns of those travelling into Central London might be different. Once in Central London
people might tend to make additional trips leading to more trips per day. We find that nearly
20% of those respondents making at least one trip have travelled into Central London, on the
survey day.
3.4.3 Effects on trips per day
The results of the empirical analysis on trip numbers using the ordered probit analysis are
presented in Table 3.8. We specify four models for trip frequency in this paper. The first
model includes all respondents whether they make trips or not. In the second model work
trips only are used as the dependent variable while in the third model leisure and shopping
trips are considered. The fourth model has again total trips as dependent variable but
excludes those making no trips. This is in order to investigate whether mobile phone
possession and telecommuting has the same effect if we consider only those who leave their
house at least once per day. Additionally, our public transport variable and those who make
at least one trip destination into the Central London are included in the model. These
variables are excluded in the first, second and third models for reasons of logical consistency,
e.g., those who do not make any trip naturally will not use public neither private transport.
Otherwise, to allow for a better comparison, the fourth model is a replica of the first model.
The McFadden R2 values are also presented in Table 3.8, which are found to be small, but
comparable to other applications of ordered probit analyses in transportation with low R2
value (e.g., Bhattacharjee et al. 1997, Khattak et al. 1993 and Quddus et al. 2002). For this
reason, the discussion will focus mainly on most explanatory variables that exhibit significant
80
t-values.
Table 3.8 shows that females tend to make more total and leisure trips but less work trips.
Those aged 35-44 and aged 65-74 tend to make most total trips. Households with children,
in particular those being married and having children tend to make most trips in all models.
For married with dependent children, they make more total trips while less work and leisure
trips. Presumably, the reason for this is that those married with dependent children usually
make additional other trips such as dropping off or picking up children. In all models, white
people tend to make more trips than non-white. Furthermore, car license has a positive
effect in all models. Comparing this to existing literatures (e.g. Schmöcker et al, 2009; Lu
and Pas, 1999), all these results are as expected.
Surprisingly, car ownership is negatively associated with the number of leisure trips (Model
3) as well as to those who are making at least 1 trip (Model 4) but not significant in the other
two models. The reason behind the effect in Model 3 might be due to work day effects since
car based leisure trips are mainly carried out in weekends. Our result in Model 4 is further
qualified by the finding that using public transport has the expected (and more significant)
negative effect on trip numbers.
The number of trips increases for a respondent who has destinations within Central London,
as observed among those who are making at least one trip per day (Model 4). Further, those
living in Outer London tend to make more total trips than those living in Inner London
(Models 1 and 4). Outer Londoners in particular tend to make leisure and shopping trips
(Model 3). This might be because in Outer London there are still more local shopping
streets with easy access which invite shoppers to make additional trips. In contrast, those
residing in Inner London are possibly more often travelling to larger shopping centers
resulting in less leisure and shopping trips. For population density we find similar effects as
for Inner and Outer London dummy variable. Those living in the most densely populated
areas tend to make more leisure trips (Model 2) and more total trips if they leave their house
during the day (Model 4). Population density is not of significance in Models 1 and 2.
Though the discussion of the effects of our control variable could be extended in the
following we focus our discussion on the effects of our variables of primary interest, mobile
phone possession and telecommuting.
81
We find that mobile phone possession has a positive effect on total trips made, which
confirms our hypothesis A.1. Among those who work full time, those who do not
telecommute or do only some telecommuting tend to make more total trips than those who
telecommute a lot, mainly because of work trips (Models 1 and 2). Interestingly, though,
those who do some telecommuting make more trips than those who do not telecommute,
confirming our observations made in the crossable analysis. Among the part time working
respondents, we can see a similar negative effect of much telecommuting on daily trips.
The only difference is that those who are not telecommuting do most trips, followed by those
who do some telecommuting, with those much telecommuting making the least total trips.
Our results further indicate that much telecommuting full time workers make less work trips
which confirms our hypothesis A.2. In addition, full time workers who are not
telecommuting make less leisure trips in correspondence to our hypothesis A.3. Generally,
part timers tend to make in general more leisure trips and the effect of telecommuting is not
very pronounced. The total trips made are slightly increased when doing some
telecommuting compared to not telecommuting at all. This result confirms to hypothesis A.4.
However, our hypothesis is opposed when the respondents do much telecommuting as we
find that those working full time and are not telecommuting tend to make the least trips
among all our six categories.
The result in our work trip model (Model 2) also reveals that self-employed respondents with
higher household income are more likely to make less work trips. According to Table 3.9,
most of the self-employed have a higher household income, particularly those who do some
telecommuting. We, therefore, might presume that many of the self-employed with high
income respondents are those who have their own business or are working freelance, both of
Table 3.9 Average household income (in £) by telecommuting status
Telecommuting status
Employed respondents Self employed
Full time worker
Part time worker Full time worker
Part time worker
No 35277 27503 37439 14038
Some 47162 39007 55625 7500
Much 44719 38001 42500 22500
82
which might not require them to do regular commuting trips.
The effect of the type of employment, which is interacted with the average annual household
income, on tour number and tour complexity is further discussed in the next chapter. This is
to determine if employment has something to do with the number of tours or the number of
stops per tour.
3.5 Summary and discussion
This study investigated the effects of ICT, namely mobile phone possession and
telecommuting, on weekday trips of Londoners. The results support our expectations that
mobile phone possession tends to increase trip making. In 2001, when the survey was
conducted, mobile phone possession was probably still related to income which is much less
likely to be the case nowadays. This could explain why our results show that mobile phone
holders make more work trips. Some of the effects described in this paper might be general
trends in societies where communication is increasingly based on mobile phones. Our
results might further be of interest for some developing countries where the level of mobile
phone possession is nowadays similar to the one in London in 2001.
The results also confirm that telecommuting affects total trips. The regression analysis
suggests that those telecommuting much, make less trips per day. The trip decrease is
however much less than the reduction in work trips confirming the in the literature well
described substitution effects of telecommuting. Our analysis confirms that these
substitutions are likely to be leisure and shopping trips.
Besides telecommuting, the type of employment clearly has an effect on number of trips
made. Those being self-employed make less work trips but don’t seem to compensate these
with additional leisure or shopping trips.
83
To manage the trip substitution effects of telecommuting hence a careful design of
neighborhoods might be of increasing importance. Nearby “corner shops” and cafes within
local shopping streets could be profiting from telecommuting trends since they offer
possibility for additional spontaneous trips arranged for example by mobile phone. Our
inclusion of geographic characteristics in our analysis gives some support for such a
conclusion.
One of the limitations encountered in this study is the lack of information on the amount of
mobile phone use. It is, therefore, hoped that such information be introduced in the future
development of travel survey.
***
84
CHAPTER 4 INFORMATION AND COMMUNICATIONS TECHNOLOGY ADOPTION
AND TOUR COMPLEXITY
4.1 Introduction
This research considers two applications of information and communications technology
(ICT): mobile phone and telecommuting. As aforementioned, many aspects of people’s
lives have seen to benefit from the use of mobile phone. For instance, mobile phone is used
for emergencies where immediate contact with another party (such as, family or emergency
services) is vital (Katz, 1997). Such urgent situations can range from the national politics
campaigns in the Philippines (Pertierra, 2005) to coordination of various social activities.
Hence, one of the useful functions a mobile phone can offer to many is being an instant
device for communication in some urgent circumstances that comes any time and any place.
Compared to the previous contributions, this study seeks to investigate the substantial amount
of time of telecommuting needed that will cause a change in travel patterns. Once again,
telecommuting is defined here as the use of personal computer at home for work, certainly,
not working from other non-home and non-work places. Although there are aspects that
mobile phones and telecommuting might affect; for instance, in Chapter 3 discusses the
effects of ICT on trip frequency, this research focuses on tour numbers and tour complexity.
To a certain extent, this particular research has a resemblance with Chapter 3 only that this
research purely focuses on the on the different types of tours defined and employed in this
research as well as on tour complexity, which is basically the number of stops made per tour.
The rest of the paper is arranged as follows. Section 4.2 recapitulates some of the relevant
studies regarding the effects of mobile phone and telecommuting on tours and establishes the
hypotheses on the impacts of mobile phone possession and telecommuting on tours. Section
85
4.3 briefly describes the data used in the analysis and presents the result of the descriptive
analysis. Section 4.4 explains the empirical regression results and discusses the effects tour
types and complexity. Section 4.5 encapsulates the results of this paper and addresses
pertinent implications.
4.2 Literature review
4.2.1 Relevant studies
Several ICT applications appear to have affected the lives of people in different fundamental
ways. For instance, ICT can facilitate the scheduling of activities by sending emails or
making phone calls for constant coordination. In the review paper by Golob (2000), he
forecasts ICT effects on activity and travel. He also suggests that mobile phones and other
portable communication devices will redefine our ability to conduct business and dynamically
schedule activities while on travel or at locations away from home or workplace.
Srinivasan and Raghavender (2006) investigate the effects of mobile phones on unplanned
activity-chaining and unplanned ride sharing arranged through mobile phones. They find
that at any given instant mobile phones can lead to unplanned stops while on travel.
Schmöcker et al. (2010) investigate trip chaining among older London residents. Though
the focus of their research is not on ICT effects, their results suggest that older residents with
mobile phones have tendencies to make more complex tours. Such results appear not
limited to certain age groups.
Further, telecommuting allows people to keep away from the hassles of commuting by
reducing physical trips. Therefore, telecommuting is often suggested to be one of a series of
policy measures to reduce travel demand (e.g., Mokhtarian and Salomon 1997).
Telecommuting instead of actual commuting might, however, often reduce travel demand less
than hoped for by transport planners. Using time-series data from the national statistics
office in Canada, Norway and Sweden, Harvey and Taylor (2000) reveal that working in
isolation at home does not really diminish travel. Especially if telecommuting from home,
86
some people may get bored of their environment and rather spend more time to shop, to do
household chores or to socialize with friends.
The effects of mobile phone and telecommuting on travel are slightly discussed here. Since,
some of the relevant studies regarding ICT and travel are already presented and elaborately
discussed in the preceding chapter.
4.2.2 Hypotheses
This study hopes to play a role in the increasing studies on ICT and travel behavior. In
connection to earlier studies, this study particularly investigates the effect of having mobile
phone and telecommuting on tours a person makes. We consider tour number and tour
complexity as our dependent variables. Stemmed from the former literatures, we formulate
two groups of hypotheses: firstly on the effects on tour number; and secondly, the effects on
tour complexity. For each group, we further establish our hypothesis regarding the effect of
mobile phone possession and telecommuting. However, to this point, we find a limited
literature regarding the effect of telecommuting on tour numbers. Hence, we develop
presumptions based on some rational intuitions (A.1 below). Tours are defined in the
following as a chain of trips with home as anchor points.
A. Tour Number
A.1. Mobile phone possession might have a (weak) negative effect on (home-to-home)
tours. This is because the more complex tours of mobile phone users (C.1) might enforce
a reduction in total tours due to time and space constraints. Further, as argued above, in
some occasions mobile phone possession might make additional tours redundant.
A.2. Similarly, telecommuting from home tends to increase tour numbers. This is
because it encourages people to make more simple tour chains to break their isolated
working from their home PC (Balepur et al., 1998).
B. Tour Complexity
87
B.1. Mobile phone possession is generally likely to lead to more complex tours as
suggested by Schmöcker et al. (2010) for a limited sample of those aged over 60. Our
rationale is that access to communication while on a travel might lead to additional
unplanned stops.
(a)
(b)
Figure 4.1 Illustration of hypotheses (a) shows the hypothesis of the effect of mobile phone possession on tour number and tour complexity as stated in A.1 and B.1 (b) represents the hypothesis of the effect of telecommuting on tour numbers and tour complexity as discussed in A.2 and B.2
(Adapted from Chapter 3)
88
B.2. On the contrary, the tour complexity is likely to decrease for those who telecommute
from home. Our presumption is based on the same argument given in B.2.
These hypotheses are illustrated in Figure 4.1 and are based from Chapter 3 only that this
time the consideration of tour numbers and tour complexity is applied. Reported from the
preceding results, both telecommuting and mobile phone possession manage to make more
trips, however, these results of telecommuting and mobile phone possession might have an
opposing effect on tour complexity.
4.3 Data Structure and descriptive analysis
4.3.1 Data description
The data used for this particular analysis are extracted from the London Area Travel Survey
(LATS). The descriptive results of the variables used will be briefly described here since the
variables used are almost similar to the data sets in the preceding chapter. Given that the
data are extracted from LATS, the information gathered from the survey for each individual
involve are on the regular weekday travels in Greater London. As a review, all of the
interview procedure is done on a personal basis, and the respondents are asked to fill in a
1-day travel survey. The collected sample comprises of 67,252 individuals from the total of
29,973 households interviewed.
The gathered information are divided into four main data tables: (1) household information,
(2) individual information, (3) trips made by the individual and (4) information about the
vehicles owned by the household. Socio-demographic characteristics are extracted from (1)
and (2), this includes the information on mobile phone and personal computer possession,
employment status and the number of hours per week for PC to work from home. Any
information on the number of hours of mobile phone use is regrettably not available in the
data set.
Taking into consideration the objectives of this study, it is decided to include only the
working respondents making the sample size narrowed down to 27,634 individuals who made
89
a total of 33,809 tours on the day they were interviewed. Note that during 2001, when the
survey was conducted, mobile phone possession was still likely to be correlated with income
and hence working trips. This is the reason the analysis is focused on the working
population. The tour information includes the frequency of stops and the type of activity
chosen at the destination. Further, our following analysis in particular controls for income
and distinguishes effects of ICT on number of tours as well as on tour complexity.
4.3.2 Descriptive analysis of mobile phone impact
The percentages of the variables used in the analysis are integrated and as shown in Table 4.1.
Almost 38% of the respondents possess mobile phone. Because of the restrictions of this
research has encountered, the apparent illustration of the causal relationship between the
mobile phone usage and tour complexity is limited by the collected information for mobile
phones. In this case, mobile phone possession is the only representation that can be
extracted from the data set. In the future research, the effects of the amount of mobile phone
usage would a worthwhile endeavor to perform. Meanwhile, in order to separate income,
age, effects of mobile phone possession and the amount of time using personal computer to at
home for work - a regression analysis is performed and described in Section 4.4.
Table 4.1 Mobile phone and personal computer possession
Percentage (%)
Mobile phone possession
With 44.39
Without 55.61
Personal computer possession
With 69.03
Without 30.97
90
4.3.3 Descriptive analysis of the impact of using home PC for work
From Table 4.1, it can be seen that approximately 69% of the respondents have a personal
computer at home. It is the aim of this research to investigate the considerable number of
hours per week the respondents use their PC to work from home, hence, we assumed the
classification for telecommuting which is used in the previous chapter, that is, as much, some
or not telecommuting. From the previous classification, for full time working people, those
working with PC at home for work in more than 1 full working day (≥10 hours) is defined as
“much telecommuting”. For those who are working moderately with PC at home for work
about 1-9 hours is defined as “some telecommuting”. These might be employees or
employers who usually work from the office but take some remaining work home. For the
side of part time working people, the threshold is set to ≥4 hours as much telecommuting to
manifest the overall reduced working time while those who are working 1-3 hours with PC at
home for work are assumed as some telecommuting.
The percentages of telecommuting are presented in Table 4.4. About 58% are full time
working that do not use personal computer for work. While approximately 17% are full
time do some telecommuting and only 4% percent are full time do much telecommuting.
There are a total of 21% respondents who are part time workers, with 16% who do not use
personal computer for work at home, 3% are those who do some telecommuting and only 2%
who do much telecommuting.
4.4 Regression analysis 4.4.1 Model structure
Aforementioned in the previous chapter, ordered probit regression is the most appropriate
methodological analysis for modeling with a dependent variable that takes more than two
values, where these values have a natural ordering. Other than that, by initial investigation
of data sets used in this research, the nature of the data itself behaves in such a way that
ordered probit analysis is the most fitting method to be used. Other statistical analyses are
also applied and tested but are found not suitable for the data set to be analyzed.
91
Because the approach used here has similarities with Chapter 3, the model structure is
identical only that the observed dependent variable used is the tour complexity, instead of the
trips. To reiterate, in the ordered probit model, the dependent variable is latent and
expressed as:
𝑦𝑖∗ = 𝒙𝑖𝜷 + 𝜀𝑖, (eqn. 4.1)
where 𝑦𝑖∗ is a latent variable measuring the number of stops per tour (instead of trips in this
case) for individual i (i = 1,..., N) and N is the sample size; 𝒙𝑖 is a (k × 1) vector of
independent (observed) nonrandom explanatory variables; 𝜷 is a (𝑘 ×1) vector of unknown
(coefficients) parameters; 𝜀𝑖 is the random error term, which is assumed to be normally
distributed with zero mean and unit variance.
Let 𝑦𝑖 denote the number of observed stops per tour. To convert the continuous latent
variable 𝑦𝑖∗ into the discrete observed number stops per tour, a set of 𝝁 (n× 1) is introduced
where n denotes the number stops per tour categories as shown below:
𝑦𝑖 =
⎩⎪⎪⎪⎨
⎪⎪⎪⎧
0 if − ∞ ≤ 𝑦𝑖∗ ≤ 𝜇1
1 if 𝜇1 ≤ 𝑦𝑖∗ ≤ 𝜇2
2 if 𝜇2 ≤ 𝑦𝑖∗ ≤ 𝜇3…
𝑛 + 1 if 𝜇𝑛 ≤ 𝑦𝑖∗ ≤ ∞,
� (eqn. 4.2)
where the vector of threshold values 𝝁 are unknown parameters to be estimated along with
the parameter vector 𝜷. . In subsection 4.4.3, we deal with tour complexity by taking the
number of stops per tour as a dependent variable.
The parameters are to be estimated so that yi* is expected to change by 𝛽𝑘 for a unit change
in xik
𝑃𝑟(𝑦𝑖 = 𝑚|𝒙𝑖) = 𝐹(𝜇𝑚 − 𝒙𝑖𝜷) − 𝐹(𝜇𝑚−1 − 𝒙𝑖𝜷), (eqn. 4.3)
, holding all other variables constant. The maximum likelihood method is employed to
estimate the parameters of the model (Long, 1997). The predicted probability of the number
of stops 𝑚 for given 𝒙𝑖 is
92
where 𝐹 is the normal cumulative distribution function.
The log likelihood function is the sum of the individual log probabilities as follows
𝐿𝐿 = ∑ ∑ 𝑍𝑖𝑗log�𝐹�𝜇𝑗 − 𝒙𝑖𝜷� − 𝐹(𝜇𝑗−1 − 𝒙𝑖𝜷)�𝑛𝑗=0
𝑁𝑖=1 , (eqn. 4.4)
where Zij is an indicator variable which equals 1 if yi
= j and 0 for otherwise.
The percentage of the control values used in the model is integrated in Table 4.3. As for the
gender, male respondents are roughly 47% making the percentage of female respondents to
53%. Comparable to previous chapter, age are grouped in two 7 categories where
respondents of the age group 35-44 have the largest percentage of about 25%. The sample
comprises of nearly 80% with white ethnicity. Practically 70% of the respondents are with
car license and in each household have an average of 1.16 cars. The sample mostly consists
of married co-habiting and married with dependent children respondents with approximately
27% and 29%, respectively. It is also the discretion to perform the interaction between
household income and type of employment. The result reveals that white collar job has the
highest household income with blue collar job has the least, as expected.
Almost 30% of the respondents are public transport user on the day the survey is carried out.
Approximately 13% respondents have destination at the Central London, popularly known
today as Congestion Charging Zone (CCZ). Those who live from the Inner London
comprise of 33%. Approximately 4% of the respondents reside with a population density of
less than 40,000.
4.4.2 Effects on number of different tour types made
A tour could comprise of one trip or a series of two or more trips linked together. The most
common tour definition describes both tour anchor points to be home (Kuhnimhof et al. 2010,
Miller et al. 2005). For this study, eight tour types were considered. The first four tour
types comprise of single stop (or simple) tours while the latter four are complex tours
93
comprising of at least 2 stops. As shown in Figure 4.2, tours with single stops are:
home-work-home tour (HWH), home-shop-home (HSH), home-leisure-home (HLH) and
home-any-home (HYH), where “any” is any trip purpose except work, shop and leisure.
The four complex tours, as shown in Figure 4.5, are the following: home-shop-work-home or
home-work-shop-home (HSWH/HWSH), similarly a combination of work and leisure trips
(HLWH/HWLH), tours with ≥2 stops with no work trip and other complex tours. These
latter four tour types are distinguished in order to see whether those who do more
telecommuting combine their work trip with other activities.
Figure 4.2 Types of simple tours
Home-Work-Home (HWH)
Home-Shopping-Home (HSH)
Home-Leisure-Home (HLH)
Home-Any-Home (HYH)
Photos are taken from www.clipartguide.com
94
Figure 4.3 Types of complex tours
The effect of possession of mobile phone is investigated against the tour types mentioned
above. As shown in the cross table analysis in Table 4.2, those who have mobile phones are
making slightly more simple tours related to shopping and leisure activities (N= 33809,
t=2.386, p < 0.001). This contrasts our assumption on A.1. However, in support of our
hypothesis we also find that those with mobile phone make more in general more complex
(5)Home -Work -Shopping -Home (HWSH /HSWH )
(6) Home -Work -Leisure -Home (HWLH /HLWH )
+ more trips (except work
trip)
+ more trips (including work trip)
(7) ≥2 stops except work trip (8) Other complex tours
Photos are taken from www.clipartguide.com
95
tours, in particular complex tours (N= 33809, t=3.428,p < 0.001), in particular complex tours
that include work trips that mobile phones encourage combining work with other activities
along the way.
The effect of each type of telecommuting, as previously described in subsection 4.3.3, is
investigated according to each type of tour in Table 4.3. For full time workers, those who
do not telecommute make most HWH tours, followed by those who do some telecommuting,
while those who do much telecommuting are making the least work tours. However, those
who do much telecommuting make the most simple shopping and leisure tours (HSH and
HLH) followed by those who do some telecommuting while those who do not telecommute
make the least number of HSH and HLH tours. On the other hand, HYH tours are most
often carried out by those who do much telecommuting, followed by those who do some
telecommuting, while those who do not telecommute make the least of number of tours.
Similar to full time workers, part time workers who do not telecommute make the most HWH
tours as one would expect. Further, those who do much telecommuting make more other
simple tours and more non-work related and more complex tours without any work-related
trips. However, those who do some telecommuting make the most number of total tours
while those who do much telecommuting make the least number of total tours.
Overall, the results in the cross table analysis suggest that those who do much telecommuting
make less number of tours which corresponds to our findings regarding trip making. Those
who telecommute much make in general less often complex tours. However, this is mainly
due to less work related tours, as the number of non-work complex tours increases. In
accordance to our hypothesis, we find that non-work related tours increase among those
telecommuting much. Our hypothesis A.2 that telecommuting in general leads to more
home-to-home tours is, however, not supported as the increase in non-work tours does not
outweigh the reduction in work-related tours.
96
Tabl
e 4.
2 E
ffec
ts o
f m
obil
e ph
one
poss
essi
on o
n th
e av
erag
e nu
mbe
r of
tour
s fo
r ea
ch to
ur ty
pe
Mob
ile
phon
e po
sses
sion
H
WH
H
SH
H
LH
H
YH
HS
WH
/HW
SH
##
H
LW
H/H
WL
H T
our
wit
h ≥2
st
ops
wit
h no
w
ork
trip
Oth
er
com
plex
to
urs
Tot
al
Don
’t h
ave
0.
395
0.07
5 0.
106
0.16
5 0.
017
0.02
2 0.
058
0.14
6 1.
320
Hav
e
0.38
2 0.
084
0.11
1 0.
163
0.01
4 0.
024
0.05
8 0.
160
1.35
7 ##
w
here
Y is
any
thin
g ex
cept
wor
k, le
isur
e, a
nd s
hopp
ing
trip
Tabl
e 4.
3 E
ffec
ts o
f w
ork
type
and
tele
com
mut
ing
stat
us o
n th
e av
erag
e nu
mbe
r of
tour
s fo
r ea
ch to
ur ty
pe
Wor
k ty
pe
and
Tel
ecom
mut
ing
stat
us
HW
H
HS
H
HL
H
HY
HH
SW
H/
##
HW
SH
H
LW
H/
HW
LH
Tou
r w
ith
≥2
stop
s w
ith
no w
ork
trip
Oth
er
com
plex
to
urs
Tot
al
Full
tim
e w
orki
ng, d
o no
t use
PC
for
wor
k 0.
456
0.06
1 0.
100
0.12
1 0.
015
0.02
5 0.
045
0.16
1 0.
9832
Full
tim
e w
orki
ng, u
ses
PC
for
wor
k
1-9
hour
s pe
r w
eek
0.35
9 0.
062
0.11
4 0.
145
0.01
5 0.
027
0.04
7 0.
214
0.98
26
Full
tim
e w
orki
ng, u
ses
PC
for
wor
k
≥10
hour
s per
wee
k 0.
263
0.12
1 0.
131
0.22
4 0.
014
0.01
5 0.
079
0.12
6 0.
9723
Par
t tim
e w
orki
ng, d
o no
t use
PC
for
wor
k 0.
273
0.11
3 0.
115
0.28
2 0.
021
0.01
4 0.
092
0.07
8 0.
9877
Par
t tim
e w
orki
ng, u
ses
PC
for
wor
k
1-3
hour
s pe
r w
eek
0.17
6 0.
106
0.14
6 0.
321
0.01
0 0.
014
0.11
5 0.
106
0.99
26
Par
t tim
e w
orki
ng, u
ses
PC
for
wor
k
≥4 h
ours
per
wee
k 0.
108
0.15
1 0.
155
0.33
7 0.
016
0.01
0 0.
139
0.05
7 0.
9706
##w
here
Y is
any
thin
g ex
cept
wor
k, le
isur
e, a
nd s
hopp
ing
trip
97
4.4.3 Effects on tour complexity
Finally, an ordered probit model has been performed to investigate the hypothesized effects
C.1 and C.2 on tour complexity, where the number of stops is regarded as the dependent
variable (Table 4.4).
Similar to the result for total trips (result in the preceding chapter) and in accordance with
previous literature, female has a positive effect on tour complexity. People who are between
35-44 tend to make more complex tours and white people are likely to generate more stops
per tour than non-white. However, surprisingly the results show that having a car license
exhibits no significance on tour complexity. The same effect with the trip result can be
observed for household with dependent children which implies that they make more stops in
a tour, with single parent make most of the tours. Interestingly, self employed with high
income make less stops per tour. The reason for the self employed to make less stops per
tour might be because of the nature of their job that does not demand them to make a trip for
work. Car ownership and the frequency of bus stops exhibit no significance and if we
perform, the interaction between them still exhibits no significance. However, the result
showed that car users make more complex tours than the public transport user.
Comparable with the earlier trip model, we include the geographical attributes like the
congestion charging zone, area, and the population density. The result indicates that more
stops per tour are made within Central London. The reason behind this might be that most
of the respondents go to the congestion charging zone primarily for work or for various
personal business transactions. In addition, most respondents who reside from the Outer
London area make more stops. Again, perhaps respondents who reside from the outskirt of
London make more shop hopping form place to another compared to those residing in the
Inner London, where they can go to one-stop shop, say mall, for shopping or even leisure
resulting to less number of stops.
Noteworthy are however our results regarding some geographic control variables. The
results indicate that more stops per tour are made by those who travel into Central London.
The reason behind this might be that workers in Central London are more likely to combine
their work with other trip purposes before returning home. To return home after work only
98
Table 4.4 Ordered probit model on tour complexity
Percent (%) Estimate t-stat
Cut points (Tour)
0 Stops 2.11 -1.806 -30.715
1 Stops 63.70 0.681 11.889
2 + Stops 34.19 --- ---
Socio-demographic
Gender
Male =1 (female =0) 47.17 -0.125 -8. 406
Age
16-24 (reference) --- ---
25-34 22.21 0.098 3.450
35-44 24.56 0.115 3.985
45-54 16.13 0.022 0.756
55-64 11.18 0.062 1.881
65-74 9.12 0.169 2.822
75 and above 5.64 -0.046 -0.316
Race
White = 1 (Non-white= 0) 77.60 0.115 6.652
Car license
With license = 1 (No license = 0) 70.44 0.017 0.846
Car ownership 1.16 -0.013 -1.026
Household structure
Single 18.34 0.058 2.497
Single parent with dependent children 8.06 0.144 4.513
Married/co-habiting 26.60 -0.080 -4.444
Married with dependent children (reference) 28.52 --- ---
All pensioners 6.94 -0.096 -1.186
99
[Table 4.4 continued…]
All other households 11.55 -0.046 -1.937
Interaction between household income and employment type #
Household income * White collar job 45116.02 0.031 7.580
Household income * Administrative job 36530.06 0.030 5.683
Household income * Health related job 38179.21 0.023 4.727
Household income * Blue collar job 28799.07 -0.004 -0.648
Household income * Self-employed 32083.33 -0.076 -1.632
Household income * Transport-related job 29668.59 0.018 1.514
Public transport and Destination at Central London
Public transport
User = 1 (non-user = 0) 26.67 -0.224 -12.836
Destination at Central London
Within Central London = 1 (Otherwise 0) 12.85 0.287 14.466
Geographic characteristics
Area
Inner London = 1 (Outer London = 0) 32.82 -0.044 -2. 299
Population density (population/ square mile)
1000-2000 2.20 -0.001 -0.028
2000-4000 2.23 -0.205 -3.946
4000-10000 23.49 -0.008 -0.287
10000-25000 55.64 -0.048 -2.071
Over 25000 (reference) 16.44 --- ---
100
[Table 4.4 continued…]
Mobile Phone Possession
Mobile phone
With mobile phone = 1 (otherwise 0) 44.39 0.012 0.921
Telecommuting status
Full time working, do not use PC for work 58.35 0.142 4.127
Full time working, uses PC for work 1-9 hours per week 17.00 0.244 6.705
Full time working, uses PC for work ≥ 10 hours per week 4.27 --- ---
Part time working, do not use PC for work 16.07 0.140 3.653
Part time working, uses PC for work 1-3 hours per week 2.80 0.320 6.144
Part time working, uses PC for work ≥ 4 hours per week 1.51 0.001 0.013
Number of observations 33809
Log Likelihood Intercept only 45846.84
Log Likelihood Final 44847.50
Mc Fadden R 2 0.02
#
have average value instead of percent
to go out once more is probably less common among those working in Central London (and
living in Outer London). Similarly, most respondents who reside in Outer London make
more stops. This also supports our explanation mentioned previously on the difference in
shopping behavior in Inner and Outer London. Similarly, people residing in areas with
101
population density of over 25,000 sq. mi. make more stops per tour. Further, the results
show that those who use car on the day of the survey make more complex tours than those
who use public transport.
The model result also indicates that mobile phone exhibits no significance on tour complexity.
This result unlikely confirms our hypothesis B.1. Full time workers who do some
telecommuting make more stops than those who do not telecommute while those who do
much telecommuting make less number of stops. This effect holds true also for part time
workers. Those who do some telecommuting make more stops per tour than those who do
not telecommute, but those who do much telecommuting make the least complex tours. In
summary, our hypothesis B.2 is supported only for those telecommuting a lot while we
observe a contrary effect among those doing some telecommuting.
4.5 Summary and discussion
This study examined the potential effects of mobile phone possession and telecommuting, as
ICT applications, on the number of tours and tour complexity during weekdays in London.
Mobile phone possession tends to increase the number of home-to-home tours per day.
Though one might argue that 2001 data are already slightly outdated, the effects of mobile
phone will be more difficult to disentangle in the analysis of surveys carried out nowadays,
when mobile phone possession has become almost standard. Moreover, the data used is
based from a developed country which would also pose some interesting insights in the case
of some developing countries where the adoption of mobile phone nowadays similar to the
situation in London in 2001. The succeeding chapters will help enlighten the effects of ICT
in the case of developing countries.
Through ordered probit regression, the results also verify that telecommuting affects tour
numbers as well as the tour type. Moreover, we find some non-linear effects on tours made
with regards to the amount of telecommuting. Those who do a small to medium amount of
telecommuting tend to make more complex tours and almost the same number of tours
compared to those not all working at home. Only for those telecommuting a lot we can find
102
the hypothesized effects of more simple home-to-home tours.
Like in the preceding chapter, the type of employment considered in this research evidently
affects tour complexity. In particular, they appear to make less complex tours. Trip
chaining is often seen as a means to reduce total travel effort. The results suggest though
that additional freedom through telecommuting or self-employment is used to decouple
errands into several tours. It thus supports the argument that trip chaining might be rather a
burden as it requires more pre-trip planning. With increasingly more flexibility about work
place and time one might hence conclude that planned complex tours will be further
decreasing but replaced by more simple tours that might be combined with some spontaneous
activities organized en-route through ICT.
On the one hand, this might be a chance for increased uptake of public transport as our results
confirm the negative association between tour complexity and public transport usage. On
the other hand, once travelers have reached an attractive destination (such as Central London)
they clearly tend to combine this tour with many side activities. For this, again, having a car
appears to be the preferred the choice.
The amount of time of telecommuting plays a significant role to identify the cause of shift of
travel behavior. In this case, full time who do much telecommuting indicates that it reduces
tour complexity and entangles it into several simple tours. Both full time who do not
telecommute and full time who do some telecommuting have an increasing effect on tour
complexity. Likewise with part time workers, only that those who much telecommuting
exhibits no significance. With these results, one might speculate that the entangled simple
tours are tours to the “café shop” or to the gym in order to escape from isolation and from
sitting in front of the computer all day.
As a recommendation for the future works of this research, it is hoped to include the main
purpose of tour in the tour complexity analysis for the better understanding of travel behavior.
***
103
CHAPTER 5
THE EFFECTS OF SOCIAL INTERACTION AND SOCIAL NETWORK
ON TRAVEL BEHAVIOR
5.1 Introduction
The emergence of modern lifestyles has included dramatic changes in areas of work, leisure,
and travel. According to Urry (2007), the German sociologist Georg Simmel argued that
people travel to social destinations (social activity-travel) for two reasons: (1) they are
attracted to others for ulterior reasons and (2) they enjoy engaging in “free-playing
sociability,” namely forms of social interactions that are free from content, substance, and
ulterior end. Such social interactions within certain social structures can produce obligations
and expectations of reciprocity (Hibbit, Jones, & Meegan, 2001), which will eventually allow
individuals to exchange information and influence one another’s behavior (Avineri, 2006). As
interactions with acquaintances intensify (Brueckner, 2006), individuals may not behave as
independent entities in society (Blumer, 1969).
Generally, in social life, the apparent need or obligation to travel emanates from the attractions
and pleasures of socialization that are necessary for participation in social life . Tannenbaum &
McLeod (1967) noted that the concept of socialization has been expanded from the
socialization of children into their cultural environment to include “adult socialization” and a
wider variety of phenomena (e.g., assimilation into various formal and less formal social
roles). Here, I use the term “socialization” to refer to the various forms of social interaction
or communication that university students typically perform with other social contacts,
particularly through the use of information communication technology (ICT).
104
Although initial explorations of the effects of certain social factors on travel behavior have
been conducted in some developed countries, this research agenda is relatively new. In a
study done in Switzerland, Axhausen (2003) hypothesized that travel behavior is mainly
shaped by an individual’s social networks, made up of family, friends, and work associates.
Technically speaking, social network is defined as a set of actors and the ties among them
(Wasserman & Faust, 1994). People travel due to commitments to their families, friends, and
work. Recent studies by Carrasco and Miller (2006) and by Carrasco et al. (2006) have
examined social activity-travel within entangled social networks in Canada. Those studies
analyzed the relationships among social networks, activity-travel behavior and slightly ICT,
with particular focus on two concepts from sociology: social accessibility and agency. These
concepts were found to be relevant to travel behavior.
By studying the relationships between social factors and travel behavior, it is possible to
investigate the social processes that initiate and harmonize with the functions of transport
systems. For instance, patterns of visiting friends or other forms of movement (e.g.,
university or work patterns) might depend on the available transportation infrastructure (e.g.,
public roads and railway systems). Thus, social processes might orchestrate and blend with
available transport systems. Traditional approaches to transport study have primarily
examined economic motives (e.g., reducing travel cost or time) and psychological factors that
depend mainly on personal motives. However, few studies have related social factors to
travel behavior, and those studies have examined in the developed countries such as
Switzerland and Canada. The present chapter is pioneering in that it investigates social
factors related to activity-travel behavior in a developing country.
The purpose of this chapter is to investigate the activity-travel behavior of university students
as related to their patterns of socialization. This chapter focuses on young cohorts in Metro
Manila, the Philippines, for several reasons. First, Filipinos have been characterized as
culturally sociable and as frequently keeping in touch with family and friends (Salazar, 2007).
If people are sociable, it is possible to suppose that the degree of socialization is high and can
be examined for its relationship to activity-travel behavior. Interpersonal interaction regulates
the shared cognitive meanings (Scweder and LeVine, 1984), the behavioral patterns
(Goodenough, 1970), and the belief and value systems (Triandis, 1972) of the collective
society (Dadkhah et al., 1999).
105
Figure 5.1 Mobile cellular subscribers as a percent of total telephone subscribers,
selected countries, 1996
Source: (ITU World Telecommunication Development, 1998)
Second, as mentioned above, most prior studies relating travel behavior to socialization were
based on experiences in developed countries (Axhausen, 2003; Carrasco et al, 2006; Carrasco
& Miller, 2006). With the data in 1996 presented in Figure 5.1, it has shown that mobile
phone has had mixed success in enhancing universal access in the developing world. Mobile
phones act as a substitute in the developing countries where fixed lines are found to be
inaccessible and expensive. Substitution typically occurs where relatively low levels of
landline (main line telephone) density are combined with competitive mobile phone markets.
Mobile phone solutions are also being used to increase accessibility in remote, rural or
otherwise disadvantaged areas.
In the case of the Philippines, the number of mobile phone subscribers has gradually
increased from 1995 to 2005, as presented in Table 5.1. Most noticeably, there is a remarkable
increase of mobile phone density from 2000 to 2001 which almost doubled the mobile phone
density from 8.46 to 15.61.
106
Table 5.1 Number of Mobile phone subscribers in the Philippines from 1996-2005
Year Number of Mobile Phone subscribers
Growth rate Mobile density
1996 959,024 1.37
1997 1,343,620 40.1 1.87
1998 1,733,652 29.03 2.27
1999 2,849,880 64.39 3.8
2000 6,454,359 126.48 8.46
2001 12,159,163 88.39 15.61
2002 15,383,001 26.51 19.36
2003 22,509,560 46.33 27.77
2004 32,935,875 46.32 39.85
2005 34,778,995 5.6 41.3
Source: : National Telecommunications Commission (NTC), Philippines (2005)
In addition, according to ITU World Telecommunication (2008), the number of mobile phone
subscribers in 2006-2007 is approximately 43 million, which happened to be comparable to
the number of mobile phone subscribers in case of UK in 2001, the year when LATS was
conducted.
Third, according to Hyodo et al. (2005) in their study on the characteristics of travel behavior
on selected thirteen (13) urban cities, young cohorts in Metro Manila travel more often than
older cohorts, as illustrated in Figure 5.2, and thus provide a good opportunity for examining
whether socialization of this young group of people influences their trip generation.
Apart from being the age-group that travels more often, it is also opted for a qualitative
sample based on a particular social group with seemingly rich and varied combinations of
forms of sociability and cultural engagement with extensive contact with screens, use of a
computer with Internet access (at a café, at home or at the university), use of various functions
107
of mobile telephony, phone (Carroll, et al., 2002), and attendance of higher education
institutions (Héran, 1988). Since the distribution of cultural practices and social networks
corresponds, cumulatively, to an individual’s cultural capital, the weight of the cultural capital
inherited from their social background and acquired from higher education institutions (e.g.
Wyn & Stokes, 2005) makes this group particularly well-suited to the type of questioning
implemented in this research.
Hence, it is hypothesized that socialization might significantly affect young people’s travel
behavior. I use the frequency of socialization later in this chapter to analyze social activity-
travel patterns versus overall trip frequency as students returned home after class. Generally,
the only time that students could freely engage in socialization involving travel was after
classes had ended for the day. Hence, the number of side-trips while returning home was used
78.59 74.5366.85 66.90 70.31 74.51
7.256.16
5.56 5.995.86
6.50
14.02 19.1727.46 26.97 23.65 18.81
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
<20 20-29 30-39 40-49 50-59 >60
>4
3
2
1
Figure 5.2 Number of trips according to age group of the respondents Source:JICA- MMUTIS, 1999
Age groups
Number of trips
Per
cent
age
(%)
108
as the dependent variable, and was defined as the total number of side-trips taken between
school and home. For example, after leaving school, a student might visit the sports club, go
shopping, and then finally arrive home. Because trips to school generally followed a regular
schedule, I did not consider those trips in this analysis.
To collect data on social activity-travel behavior and to examine social networks by travel
behavior analysis, I follow the methodology proposed by Carrasco et al. (2006). Their
method is based on an “egocentric approach,” whereby personal data are collected from
individuals. The egocentric approach can reveal detailed characteristics of each respondent’s
social network. For instance, respondents may reveal whom they consider to be their close
friends and with whom they socialize more often. In other words, each respondent discloses
his or her own social network. To untangle the composition of a social network, an “ego” and
his or her “alters” should be defined. The respondent is the “ego” and has a network of
friends, termed “alters.” In this chapter, I use the terms ego/respondent and friends/alters
interchangeably and apply the egocentric approach.
The remainder of this chapter is structured as follows. Section 2 discusses the study
framework and general hypothesis. Section 3 describes the survey design, research method,
and some descriptive statistical results of the survey. Section 4 discusses the findings of the
empirical model. Finally, Section 5 presents the conclusion and recommendations.
5.2 General hypothesis
Within the context of social interactions, a number of elements may lead to trip generation,
including geographic, economic, psychological, and demographic characteristics; physical
attributes; and attitudes regarding travel. I hypothesize that opportunities for socialization
might be a factor that considerably affects the travel behavior of young cohorts in low-income
regions like Metro Manila. On the basis of a review of current literature, I develop the
conceptual model of socialization and travel frequency shown in Figure 5.1. Socialization
may encompass an array of forms including physical interaction (face-to-face interaction),
virtual interaction (e.g., mobile phone call, text message), and a series of social contact
109
Figure 5.3 Proposed exploratory factors influencing after-class side-trips
extensions (social network). Trips also have various purposes and can be made for leisure, to
return home, for shopping, or for work. As Carrasco et al. (2006) suggested, apart from
physical attributes, social network attributes and frequency of interaction are propensity
factors that help link a set of different potential causes for the generation of social activities.
Travel behavior
Text messaging
Landline calls
Face-to-face interaction
Sending letters/cards
Cell phone calls
Social network
Online chat
Socio-economic and socio-demographic characteristics
+
+
+
-
-
-
--
110
I further hypothesize that the number of face-to-face interactions might positively affect social
activity-travel. For instance, repeated face-to-face interactions should eventually lead to
travel to another location for co-present meetings with family members or friends. As Urry
(2003) noted, the sense of normal co-presence of family members requires intermittent travel
so that family members can keep in touch. I assume that email and online chatting are often
used to communicate with distant connections or a sparsely distributed social network and
could substitute for long-distance travel; hence, these communication technologies would
reduce travel. Sending letters or cards might also reduce travel. However, the rise of
electronic information technology has decreased the popularity of letters and cards among
young people. Mobile phones are now ubiquitous in Metro Manila, although calls are more
expensive and thus rarer than text messages. I also assume that mobile phone use would
result in diminished travel. The same effect on travel was assumed for landline calls.
Most Filipino families rely more heavily on mobile phones than on landline phones. Landline
phones are more inflexible to use, are limited to one-to-one talk, and often do not produce an
archived entry Larsen et al. (2006), making them a less appealing tool among university
students for organizing activities. At the same time, the existence of a social network was
expected to increase travel. Logically, a larger network should mean greater chances to travel.
This presumption is supported by the suggestion of Carrasco et al., (2006) that the
communication patterns of an individual and social activity patterns emerge and can be
partially inferred from his or her social networks.
I construct our hypothesis on the relationship between socialization and travel based on (Urry,
2003) suggestion that transport is mostly a means to certain socially patterned activities.
Apart from opportunities for socialization, socio-economic and socio-demographics might
also have significant causal effects on travel. Section 4 describes the model specifications in
greater detail.
111
5.3 Overview of the study area
5.3.1 Overview of Metro Manila
The study is carried out in Metro Manila which is a metropolitan region that comprises of 16
cities of which the city of Manila is the capital of the Philippines. Metro Manila is the center
of political, economic, social, culture and education. The universities where the survey was
conducted were all located in the heart of Metro Manila. As illustrated in Figure 5.4, the
upper right is the place where the University of the Philippines (UP) Diliman is located and
the middle part is the so-called University belt where the Far Eastern University (FEU) and
Polytechnic University of the Philippines (PUP) are located.
Figure 5.4 Map of the study area
Photos taken from
(http://www.lakbaypilipinas.com/philippines_map.html, 2010)
112
Because of being the center of economic and finance, many Filipinos flock to Metro Manila
for employment or for economic reasons making it the most populous of all the metropolitan
areas in the Philippines. In Figure 5.5, the population density in Metro Manila in 1996 is
approximately 15,000 per square kilometer. However, this has drastically increased in 2008
with a population density of roughly 18,500 per square kilometer.
Of the nearly 12 million total population of Metro Manila, there are approximately 4 million
who are in the working population. As presented in Table 5.1, 2.3 million are male workers
while 1.8 million are female workers. However, for this particular research I did not limit to
gather samples from the working population. It is intended to include those are current
students of the universities within Metro Manila.
Figure 5.5 Population densities in Metro Manila (1996-2008)
Source: ITU, 2008
1400014500150001550016000165001700017500180001850019000 population density
Metro Manila
Pop
ulat
ion
per
squa
re k
ilom
eter
Year (1996 – 2008)
113
Figure 5.6 Working population by gender in Metro Manila (year 2007)
Source: http://www.bles.dole.gov.ph (2007)
Figure 5.7 Mobile phone, landline, internet subscription and per capita in the Philippines
Source: ITU, 2008
2287000 (56%)
1783000 (44%)
male
female
959
12159.2
68117
200
10200
20200
30200
40200
50200
60200
70200
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
mobile phone subscribers
landline phone subscribers
internet subscribers
per capita (USD)
year
Fre
quen
cy
114
Table 5.2 Household car ownership in Metro Manila
Percentage of Households
Household car ownership 1980 1996
Car-owning household 9.5 19.7
Multiple car-owning household 19 20.1
Source: MMUTIS, 1996
The three basic statistics of ICT, namely: mobile phone, landline and internet subscriptions,
are illustrated in Figure 5.6. Indeed, only the mobile phone subscription in the entire
Philippines has dramatically multiplied from 1996 up to 2008. Other ICT indicators such as
landline and internet subscription remain at low penetration rate even until 2008. In addition,
these data collected are for the whole ICT subscription in the Philippines; so far, specific data
of these ICT indicators solely for Metro Manila is hardly available. By intuition, there are
likely more ICT users in Metro Manila than in any other metropolitan region.
According to MMUTIS, the number of car ownership in each household in Metro Manila has
also increased from 9.5 % in 1980 data increased to 19.7% in 1996 data. The same is true for
those household that owned multiple cars, it has increased from 19% in 1980 to 20.1% in
1996, though the increase is not that immense.
5.4 Survey method
I collected survey data in the Philippines, focusing mainly on university students in Metro
Manila. The survey was randomly conducted from 19 to 21 March 2007 during daytime in a
115
university setting; this setting was chosen to make it easy for the respondents to answer the
questionnaire as university students. Only students present during the day of the survey were
given the questionnaire; survey sheets were not taken home to be completed at another time.
5.4.1 The sample
The survey objective was to gain information on the travel activity patterns of students from
state and private universities in Metro Manila. I pre-selected four universities for the pilot
survey: two state universities, the University of the Philippines (UP) and Polytechnic
University of the Philippines (PUP); and two private universities, De La Salle University
(DLSU) and Far Eastern University–East Asia College (FEU-EAC). All of the universities
were located within Metro Manila. As illustrated in Figure 5.4, UP is located far north, FEU
and PUP, adjacent with each other, situated in the heart of the university belt of Metro Manila
while DLSU is a little bit in the southern part. In total, I distributed and collected the data
from 304 survey respondents. The surveys of 7 respondents who did not own or did not use
mobile phones were excluded from the analysis, decreasing the total sample number to 297.
The sample total was further trimmed to 287 samples after thorough rechecking of the
completed surveys.
Table 5.2 lists and summarizes descriptive statistics for the respondents. The average age of
the respondents was nearly 20 years old, placing them in the age group of 15–24 year olds;
this age group makes up approximately 21% (2,061,407) of Metro Manila’s total population
of 9,932,560 (NSO-Philippines, 2000). The survey sample included more males (71.1%) than
females (28.9%). Similar number of surveys were obtained at each of the four targeted
universities: 29% (83 surveys) from DLSU-Manila, 22% (63) from FEU-EAC, 24% (70) from
PUP-Manila, and 25% (71) from UP-Diliman. The distribution of samples with respect to the
type of university (i.e., state or private) was also fairly equal: 49% (141) from state
universities and 51% (146) from private universities. The mean number of friends in a social
network for a single respondent was approximately 24 people; this included all categories of
friends (i.e., friends for important matters, friends for socializing, friends for advice, and
friends for small matters).
116
Table 5.3 Descriptive statistics of the respondents (N=287)
Age M =19.96, SD =1.328
Gender
Male 204 (71.1%)
Female 83 (28.9%)
University
DLSU-Manila 83 (28.9%)
FEU-EAC 63 (22.0%)
PUP-Manila 70 (24.4%)
UP-Diliman 71 (24.7%)
Type of university
State university 141 (49.1%)
Private university 146 (50.9%)
Type of residence
Parents’ house 152 (53.15)
Not parents’ house (e.g., dormitory) 134 (46.85)
Social network composition M = 23.45, SD = 13.03
M: mean, SD: standard deviation
5.4.2 The questionnaire
5.4.2.1 The main body of the questionnaire
The first part of the questionnaire was composed of five sections. The first section inquired
about socio-demographic information, including type of residence, daily allowance, and
family size. The second section obtained information on mobile phone. Questions included
whether the respondents owned a mobile phone, how many mobile phones they owned, how
many of their friends had mobile phones, and whether respondents and their friends used the
Socio-demographic
117
same mobile phone network. The use of similar mobile phone networks may impact the
frequency of texting because most mobile phone companies offer pricing promotions for
certain quantities of text messaging.
The third section canvassed respondents on their perceptions of travel, communication, and
friends. For instance, one question asked whether respondents felt that communication (using
a mobile phone) encouraged them to travel. Another question asked respondents if they felt
that communication (using a mobile phone) encouraged them to make or widen their set of
friends.
The fourth section was divided into two parts. The first part was designed to reveal the
frequencies of typical social interactions, such as text messaging, mobile phone calls, landline
calls, online chatting, sending email, sending letters, acquaintances, and face-to-face
interactions were considered. The frequency of text messages was determined by asking
respondents how many text messages they sent daily, how many people they usually sent text
messages to per day, and to whom they usually sent text messages. The frequencies of other
forms of socializing were determined in the same way as for text messages. The second part
of the fourth section focused on the frequency of social activities of the ego and his or her
alters. For instance, survey questions asked about the frequency of shopping, dining, or
attending meetings with friends; how many persons participated in each activity; for whom
the activity was conducted; and with whom each type of activity was conducted. Objectives
of this section were supposed to interpret alters by name and to reveal ego-alter interactions
and activities.
Finally, the fifth section asked about daily travel behavior on weekdays and weekends and for
school and leisure trips. Questions included the number of trips to the university, number of
side-trips taken while returning home, travel time to the university, the mode used to travel to
the university, and the fare for travel.
118
5.4.2.2 The name generator
The second part of the questionnaire was mainly for name generation, as shown in Figure 5.8.
Name generation is crucial task for eliciting the social network of respondents. Name
generation requires respondents to recall all of their friends; this process is known as the
egocentric approach. Name generation can be used to measure the strength of ties between
egos and alters and between alter-alter pairs (Carrasco, Hogan, Wellman, & Miller, 2006).
Also, it is employed in telephone sociability to study the increasing complexity of social
behaviors where news dimensions (living along and living outside the world of work) were
incorporated (Riviere & Amy, 2002).
For this survey, I refer to the egocentric process as the name generator, which simply elicited
names for members of the respondent’s (ego) circle of friends (alters). Friends were classified
into four types: (1) friends for important matters, with whom the respondents could discuss
important issues or problems; (2) friends for socializing, with whom they could go out for
social activities, such as to play sports or go to parties; (3) friends for advice, from whom they
could seek advice or counsel for issues such as school matters and job opportunities; and (4)
friends for small services, from whom the respondents could borrow small amounts of money,
class notes, or equipment.
Thirty blank spaces were provided for each type of alter, but respondents were not required to
fill all the blank spaces. The respondents were also asked to categorize each alter by age and
relationship. Although other characteristics could be added, such as occupation or
organizational affiliation, it is reasonable to assume that student respondents would have
friends who were students also. Thus I reduced the characterization of alters to age and
relationship only.
119
Figu
re 5
.8 S
ampl
e of
nam
e ge
nera
tor
used
in th
e su
rvey
120
5.5 Empirical analysis
5.5.1 Structure of the empirical path model
Structural equation modeling (SEM) uses multiple observed indicators to measure latent
variables in a model. Path analysis is a special case of SEM that uses only observed variables
(Golob, 2003) to empirically examine sets of relationships represented in the form of linear
causal-relationship models (Bollen, 1989) and to reveal direct and indirect effects of
variables. Path analysis breaks down the empirical correlations or covariances among the
measured variables to estimate the path coefficients in the path diagram. This type of analysis
can be used to test theoretical models of the causal relationships among a set of observed
variables.
In this study, various types of socializations might be causally related to the number of side-
trips taken on the return trip home. Hence, path analysis is a suitable approach for measuring
such relationships. For the path analysis, I used standardized variables to test the multivariate
relationship between socialization and side-trips made on the way home from university.
Figure 5.9 presents a schematic diagram of the path model. The figure is divided into two
sides, with the left side showing the socialization considered in the survey, and the right side
showing the number of side-trips made during the return home. Although various forms of
Table 5.4 gives the means and standard deviations for the selected variables after they had
been tested for model fit. The means of other variables tested but did exhibit the best model
fit are omitted from the table for brevity and conciseness of the variable presentation however
available from the researcher for any further verification and clarification.
121
Figu
re 5
.9 E
stim
ated
cau
sal r
elat
ions
hip
mod
el o
f so
cial
izat
ion
and
num
ber
of s
ide-
trip
s ta
ken
on th
e w
ay h
ome
Chi
-squ
are
= 9
09.9
2
d.f
. = 1
0p
<0.
01
Goo
dne
ss o
f fi
t ind
ex (G
FI)
0.93
3
Ad
just
ed g
ood
ness
of
fit i
ndex
(AG
FI)
0.83
3
Com
para
tive
fit
ind
ex (
CF
I)0.
934
Nor
med
fit i
ndex
(NF
I)0.
928
Non
-nor
med
fit i
ndex
(N
NF
I)0.
891
Leg
end
:
** s
igni
fica
nt a
t the
0.0
01 l
evel
* si
gnif
ican
t at
the
0.01
leve
l
( )
t val
ues
Soc
ializ
atio
n fa
ctor
sN
umbe
r of
sid
e-tr
ips
on t
he w
ay h
ome
TR
IHO
M
β 31
= 0
.188
**
(5.5
6)
λ 11=
0.73
0**
(16.
2)
β 32
=0.
0610
*
(2.9
6)
λ 31=
0.77
5**
(22.
8)
λ 21
= 0
.508
**
(10.
2)
λ 22
= 0
.143
*
(2.9
0)
TX
FT
F
SO
CN
ET
CH
A
122
(1) The frequency of text messaging per day (TX) had a direct effect on the number of people
with whom participants engaged face to face per day (FTF) and number of social contacts
(SOCNET), extracted from the name generator survey.
(2) The frequency of online chatting per day (CHA) had a significant direct effect on the
number of social contacts (SOCNET).
(3) The number of people with whom participants engaged face to face per day (FTF) had a
direct effect on the number of side-trips taken on the way home (TRIHOM).
(4) The number of social contacts (SOCNET) had a positive effect on the number of side-
trips taken on the way home (TRIHOM).
(5) The number of people with whom participants engaged face to face per day (FTF) and the
number of social contacts (SOCNET) mediated the relationships among the number of
side-trips taken on the way home (TRIHOM), the frequency of text messaging (TX), and
the frequency of online chatting (CHA).
Table 5.4 Descriptive statistics of the variables used for path analysis (N=287)
Variables (Definitions) Mean Standard Deviation
TRIHOM (number of side-trips taken on the way home) 2.71 1.74
SOCNET (number of social contacts, extracted from the name generator survey)
23.5 13.0
TX (frequency of text messaging per day) 55.8 50.1
FTF (number of people with whom participant engaged face to face per day)
63.8 48.2
CHA (frequency of online chatting per day) 16.3 29.4
123
The socialization and number of side-trips model (Figure 5.9) is a recursive-path model,
which can be expressed in general form by the following structural equation:
y = βy + λx + ζ, (eqn. 5.1)
where
y = p × l vector of observed dependent variables measured without error,
β = m × m matrix of coefficients relating p dependent variables to one another,
x = q × l vector of observed independent variables measured without error,
λ = m × n matrix of coefficients relating q independent variables to p dependent variables, and
ζ = p × l vector of errors in the equation.
The path model is also presented as
11 1
21 22 2
31 32 31 3
FTF 0 0 0 FTF 0TX
SOCNET 0 0 0 SOCNETCHA
TRIHOM 0 TRIHOM 0
λ ςλ λ ς
β β λ ς
= ⋅ + ⋅ +
. (eqn. 5.2)
Certain types of socializations were suggested to have a direct influence on number of side-
trips. The frequency of side-trips on the way home directly related to how much the
university students socialized face to face, how often they sent text messages, and the size of
their social network. Those who made several side-trips before reaching home were those
who had more frequent face-to-face interactions with several people and who sent text
messages more often. Sending text messages is expected to have a direct and significant
effect on the number of people with whom a respondent interacts face-to-face as well as on
the size of the social network. Srinivasan and Raghavender (2006) suggested that mobile
phone users who reported increased personal (face-to-face) meetings associated with mobile
phone use had a greater propensity to make unplanned stops during travel. Hence, I assumed
124
that the frequency of text messaging would induce more side-trips. Moreover, increased use
of text messaging might lead to changes in the location, timing, and duration of people’s
activities, and widespread use of this technology will likely be associated with new spatio-
temporal patterns of activity and travel (Kwan, 2002; Dijst & Kwan, 2004). Text messaging
can help to nurture established friendships and also to find and experiment with new
friendships and amorous relationships through friendship extensions in a social network.
Furthermore, frequent side-trips can be expected when the size of the social network is large.
For instance, if a person has several sets of friends, he or she may have more chances to make
side-trips than a person with only a few sets of friends. Grasping a social network is a
complicated task, but it is important in understanding travel behavior. By using a customized
name generator, I were able to collect information on respondents’ social networks in a
straightforward manner that was also methodologically feasible. As shown in Figure 5.8, the
list of friends are sorted into four categories: friends for important matters, friends for
socializing, friends for advice and friends for small matters. The social network variable is
defined as the sum of these four categories of friends.
Figure 5.9 also reveals the role of online chatting as it directly affects social network size.
This role may be more effective in a sparsely distributed circle of friends (especially for
friends who live in different countries), for whom text messaging is less likely because of high
costs and online chatting would allow for interaction at a more reasonable cost. Online
chatting could also enhance interactions among large groups and sparsely distributed social
contacts. In this model, only the frequency of text messaging has a direct effect on the
number of people with whom an individual engages in face-to-face interactions. For
university students, a convenient way to arrange a meeting with someone personally may be
to communicate with that person first through text messaging, followed by the actual meeting
in person as a form of positive response.
125
5.6 Results and discussion
Path analysis was performed to estimate how university students’ patterns of socialization
affect their activity-travel behavior. Figure 5.9 presents the path analysis that gave the best-fit
model.
The goodness-of-fit index (GFI) for the suggested model was 0.933, which is greater than
appropriate minimum value of 0.90 suggested by (Bollen, 1989). The GFI adjusted for
degrees of freedom (AGFI) was 0.833, which is also higher than the suggested tolerable AGFI
value of 0.80 (Cole, 1987). Hence, both the GFI and AGFI results suggest good fit of the data
to the model. Figure 5.9 also provides supplementary goodness-of-fit indices, together with
the comparative fit index (CFI), the normed fit index (NFI), and the non-normed fit index
(NNFI). CFI was 0.934, NFI was 0.928, and NNFI was 0.891. The NFI and CFI values
exceed the 0.90 cutoff (Loehlin, 1998), whereas the NNFI value is close to the 0.90 cutoff,
indicating a fit very close to the acceptable level between the model and the data.
Figure 5.9 also presents the standardized coefficients and their t-values. In addition to text
messaging, which showed a strong and significant direct effect on the number of side-trips
while returning home (p < 0.01), chatting online also had a somewhat significant effect on the
number of social contacts. Some types of socialization had a positive and significant effect on
the number of side-trips taken on the way home. The frequency of text messaging had a
direct and significant positive effect on the number of people with whom participants
interacted face to face per day, as well as on the number of social contacts. This suggests that
frequent interaction through text messaging may lead to more face-to-face encounters and
would enhance the set of social contacts.
In addition, the number of people with whom participants interacted face to face and the
number of social contacts both had direct and significant positive effects on the number of
side-trips taken while returning home. This result implies that such travel facilitates face-to-
face interactions with a large number of social contacts. The composition of a social network
also affects by socializing activities such as text messaging and online chatting. For example,
a person sending text messages to a large social network may be more likely to send more text
messages per day. Sending larger numbers of text messages, in turn, may lead to a greater
likelihood of face-to-face interaction, social networks would likely expand and more social
trips would be generated. The influence of text-messaging frequency on number of side-trips
126
while returning home can also be seen through its effect on the number of people with whom
one interacts face to face and the number of social contacts. These results indicate that text
messaging is an important functional form of socializing for university students in Metro
Manila and facilitates the generation of physical trips.
In the past, socialization and its importance for transportation infrastructure and planning
policies have been overlooked. However, the concept of socialization is now attracting
research interest in the field of transportation, particularly as ICT is making communication
readily available and literally at people’s fingertips and is also relevant to their travel
behavior. Consequently, the unseen growth of various forms of socialization could eventually
be translated into forms of trips that were previously overlooked in forecasting travel. From
the transportation perspective, path analysis could provide suggestions for transportation
planning, especially in developing regions where the current facilities, in both ICT and
transportation, require more attention.
This study of university students in Metro Manila has shown that socialization can affect
travel behavior; specifically, socialization was found to be an inducing factor and catalyst to
undertake social activity travel. Although socialization may be perceived as having a small
overall effect on travel in a zone, when the effects are combined, socialization could have
large impacts on transportation infrastructure and policies. The results of this study indicate
that socialization motivated university students to engage in social activity trips. The
approach used here and the results should contribute to transportation planning processes.
Although there might be other possibilities that may cause travel behavior of the students.
Other possibilities that may cause the propensity to engage in social activities may also
depend on personal attributes. For example, age, gender, income, lifecycle, personality, and
household characteristics. These personal attributes were also incorporated in the analysis but
found out to have no effect on the model hence it was decided to remove them. The reason of
these personal attributes to have no effect on the model might be because the respondents are
all students and that they might have homogenous attributes.
127
5.7 Synthesis
This chapter investigated the relationship between socialization and activity-travel behavior,
measured by the frequency of side-trips made while returning home after university classes. I
found that certain types of socialization had significant effects on trip frequencies among
university students in Metro Manila.
The analysis of data gathered from the perspective of university students in Metro Manila
indicated that various forms of socialization play important roles in trip generation. For the
number of side-trips made while heading home, direct and positive effects were found for the
number of people with whom one interacts face to face per day, the frequency of text
messaging, and the size of social networks. On the other hand, the number of people with
whom one interacts face to face and social network size mediated the relationship among text
messaging, chatting online, and side-trips on the way home. The results of this study also
imply that technologically mediated forms of communication (e.g., text messaging, online
chatting) are modes of socialization employed by university students, although online chatting
by itself does not appear to contribute to the generation of trips.
Overall, the results may imply that the opportunity to socialize might be a sound motivation
for trip generation even in developing countries; although there might also be other causes of
trip making as previously studied, which would be a good prospect for consideration when
constructing transportation planning policies. From the viewpoint of Metro Manila, text
messaging serves a vital role in daily undertakings and it is not only inexpensive, but also
convenient to use. Daily activities of individuals, in personal or other matters, have become
closely tied to the culture of sending text messages. Hence, to better understand activity-
travel behavior and motivation, the incorporation of variables related to socializing is
worthwhile as part of transportation planning and research.
***
128
CHAPTER 6
THE EFFECTS OF SOCIAL ACTIVITY TO TRAVEL BEHAVIOR AS AN
INTERMEDIATE FACTOR OF TRAVEL BEHAVIOR
6.1 Introduction
In developing countries, the study of travel behavior covering the effects of social factors may
be limited or, even more, have not yet explored. Although, in some developed countries, like
Switzerland (Axhausen, 2003) and Canada (Carrasco et al., 2006), associating social factors
to travel is already in progress.
The focus of this chapter is on the effects of social factors (social interaction, social activities
and social network) to travel, especially that new technologies are coming up with the
intentions of enhancing social interactions and social connections, which could have
significant effect on travel. Although Chapter 5 conducted an initial investigation on various
forms of socialization and their impacts on activity-travel, it has not yet incorporated the
aspect of social activities in the analysis. For this reason, the present chapter examines the
social factors that encompass the aspects of social interactions, social activities and the
composition of social contacts and their effects on travel.
With social interactions, habitual or intermittent, people are able to exchange information
(Arentze and Timmermans, 2007) and then create obligations and expectations of
reciprocation (Hibbit et al., 2001). Nowadays, social interactions are more pronounced than
in the previous decades especially because of the presence of technology-mediated interaction
like cell phone calls. The influx of information and communication technologies (ICT),
literally and figuratively, would also mean a bulk of obligations and an immense exchange of
information are expected. Another story is that, with ICTs everywhere, the frequency of some
129
personal touch interactions like face-to-face is lessen and subsequently substituted by a phone
call, thus travel is reduced or even substituted. As argued by Harvey and Taylor (2000),
working in isolation at-home (telecommuting) does not really diminish travel but simply
changes its purpose. In other words, if an individual finds low social interaction at the
workplace, at-home in the case of telecommuting, he will try to find it elsewhere
consequently generating travel.
Axhausen (2003) conducted the initial research referring to social factors and travel.
Although this study focused more on long distance travels, the core of the research hypothesis
was that people’s travel pattern is shaped by his network – a social network. Social network
is composed of relatives, colleagues from work (sports club or professional organization),
friends, and acquaintances. People travel because he has work commitments to do, he has to
see his family, or he has meetings to attend. In other words, each person (ego) is connected
by another person (alter) and consequently forms social network, which is composed of an
ego and his alters. As shown in Figure 6.1, for example, the ego is “you” and the alters are
“your friends”.
The prevalent method to collect the members of ego’s social network is called “ego-centered
approach”, which is employed in this study. This approach elicits the “alters” (your friends)
of the ego (you) and their characteristics. Each participant has to list down his friends and
then characterize them according to gender, age and sometimes according to their roles (or
relationship) to the ego. Ego-centered approach was widely used in analyzing the social
network in sociology and was found out to produce reliable network data and its structure.
Bien et al., (1991) reported that social network data which are collected with this approach
would be reliable and stable.
In this chapter, the template of collecting the members’ social network and their
characteristics is customized and matched in accordance to the context of university workers.
The university workers are intended to be the participants in the survey for the reason that it
will be easy to collect the social network data of the participants (ego) as well as the data of
his friends (alters). Since they are inside a community (the university premises) most likely,
they would have friends within their respective departments in the university who can also be
a participant of the survey. By setting this, I can also capture the responses from the
130
participant’s friends, meaning – the participants and his friends (most probably his coworkers
in the university) simultaneously can answer the survey.
Recently, Carrasco et al. (2006) made use of the ego-centered approach and suggested it as
the method of collecting social network to study social activity-travel patterns. In the follow-
up works of Carrasco and Miller (2008), they also took the ego-centered approach in one of
their analyses and stated social dimension as one of the explanatory factors to the social
activity-travel generation. This statement is in coherent with Lu and Pas (1999) who revealed
that travel behavior is better explained when the activity participation, it incorporates social
activities, is included in the analysis. In their study, activities are classified into two: (1) out-
of-home activity, which includes recreational and leisure activities (2) in-home activity
usually pertains to child rearing and household chores. For the present study, social activities
that were considered happen to be an out-of-home activity and are in conformity to the
activity classification by Lu and Pas (1999).
Figure 6.1 Schematic Image of Social network
You / ego
your friends / alters
131
6.2 Model of social factors and travel
I consider the social factors and their causal relationship to travel factors. The travel factors
to be dealt in this study are the total traveled trips per day and the total travel cost per day. I
regard the composition of these two factors as a rational proxy of representing the degree of
travel. The social factor considered are those that deal with patterns of social interactions and
activities as well as the composition of participant’s friends or participant’s social network.
Figure 6.2 Conceptual model of the study
Figure 6.2 illustrates the proposed conceptual model and the causal relationships between
social and travel factors. As depicted, it is divided into two levels. The upper level in Figure
6.2 are the social factors, which are grouped together with dashed lines, consist of frequencies
Social factors
Travel attributes
Frequency of Social activities
Frequency of Social interaction
Social network
Degree of travel
++
+
+
132
of social interactions, social activities, and size of social network. The lower level includes a
travel factor.
Hypothesis 1: Frequency of social activities Degree of Travel
The frequency of social activities in this study is the frequency of performing discretional
activities or leisure activities, except that in this study shopping at malls is included as
one of the social activities. For example, shopping can be defined as pleasurable social
activity as well as a necessary maintenance activity (Falk and Campbell, 1997). This
study considers shopping as pleasurable social activity hence it is included in the social
activities enumerated in the questionnaire. I hypothesize that the frequency of social
activities would have a positive effect to some travel factors. This hypothesis
corresponds to Lu and Pas (1999) who suggested that activity participation affects travel
behavior. For instance, when a person is engaged in the activities of the organization, he
has to make a trip in order for him to reach the destination and be part of the social
activities or events. However, when there are several activities to attend, it would also
imply that the person would make more of trips.
Hypothesis 2: Frequency of social interaction Frequency of social activities
The frequency of social interaction refers to the degree of communication to social
contacts. It is presumed in this chapter that there is a positive causal relationship between
social interaction and social activities (Carrasco and Miller, 2006) also stated that the
propensity to engage in social activities depends on the frequency of interaction. For
example, when a person is invited by his friends to join a social activity wherein he
happens to get to interact with them more often, in which the task to trail and to remind
about the invite can be made easily, would consequently make him likely to be involve in
the activity.
Hypothesis 3: Social network Frequency of social activities
The size of social network refers to the set of friends a person has and it is presumed to
have positive effects on the frequency of engaging in social activities. I also hypothesize
that social network composition has an important effect in the frequency of social
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activities (Carrasco and Miller, 2008). For example, when a person has many of friends
then he is likely to have more social functions and activities that he can participate.
Hypothesis 4: Frequency of social interaction Social network
The frequency of social interaction is presumed to have a positive effect on social
network. Axhausen (2006) subtly suggested about maintaining a social network whereby
he mentioned that in order not to weaken the ties, individuals must balance the cost and
effort to interact with them. For instance, to sustain or enrich social network a constant
interaction is essential.
6.3 Survey method
6.3.1 Questionnaire development
The survey questionnaires were distributed in some universities in Metro Manila and were
collected after three to four days. Although the survey can be answered for about 20-30
minutes at their office, the survey questionnaires were allowed to be brought home to provide
the participants ample time to answer them sincerely and completely. The cover letter is
attached as well as the survey sheet instruction in the survey questionnaires. The
questionnaire consists of two divisions: (1) the first division is the primary survey
questionnaire, and (2) the second division is the ego-centered network survey sheet.
6.3.1.1 The primary questionnaire
The primary survey questionnaire was developed to capture the socio-demographic
characteristics, social interactions and activity patterns of the participants. It comprises five
parts. The first part acquires the basic socio-demographic characteristics including the type of
residence, income range, number of years working and household size. The second part
requires the participants to disclose ICT use like mobile phone and internet usage and it
includes the inquiry on the retroactive patterns of social activities. The third part asks about
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the daily travel behavior of the participant. In this section the number of trips going to the
university, number of trips heading towards home, the primary and secondary mode of travel,
the cost of travel, travel time from home to workplace are elicited. The fourth part looks into
the perception of the participants on travel, communication and friends.
The fifth part of the questionnaire is the core of the survey and is divided into two sections.
The first section uncovers the frequencies of the social interactions mentioned in the
questionnaire. The social interactions considered are text messaging, email, online chat, cell
phone call, sending letter or invitations, landline call, and face-to-face interaction. The
frequency of social interaction is obtained for every category of relationship (e.g. of
relationship category is family member), to how many persons he interacts and who usually
initiates the interaction (e.g. I send text messages to family members 5-10 messages per day; I
send text messages to close friends 10-15 messages per day).
The second section is the pattern of social activities including the activities done on
weekends. The most typical social activities are enumerated in the questionnaire. The
participants are entitled to answer the frequency of performing the social activity (e.g. 2-times
a week), the number of accompanying persons doing a particular social activity (e.g. number
of persons when having dinner/picnic with friends usually I usually have dinner with 5
people). In the final part of this section, the planning time horizon of the activity is also
acquired (e.g. I plan to have dinner with friends 2 days before), as well as with whom and to
whom the activity is for.
6.3.1.2 The ego-centered network
The ego-centered network is labeled as the name generator in the survey questionnaire. It
elicits names of friends (alters) of the participant’s (ego) social network. In brief, participants
are required to recall the names of their friends. This technique of eliciting the social network
of the participants is the same way as in Chapter 5.
The lists of friends are sorted to four categories: first, friends for important matters, where the
participants can discuss important issues; second, friends for socializing, like going to parties;
third, friends for advice, like seek advice concerning job opportunities; and fourth, friends for
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small services, like borrow small amount of money. The significance of using these
categories is that friends are directly classified according to the strength of their ties to the
participant of the survey. Similar techniques were employed in the study of social support
networks by web and telephones (Kogov, 2006).
Other complex technique was used in Carrasco et al. (2006) that considered role numbers to
determine the tie strength in a social network for complex and multiple roles. Such a
technique was not employed in this study due to its complexity. Instead, a modified and
customized ego-centered approach was performed because it is concise and easy for the
participants. In our approach, the survey sheet provides 30 blank spaces in each category of
friends but does not necessarily require the participants to write in all the spaces provided.
The lists of friends written in each category will be characterized according to age and
relationship to the participant and additionally the estimated spatial distance between them
(participant and his friends) is reported.
6.3.2 Data
The data collection was done in the perspective of university workers (professors and staffs)
in Metro Manila, Philippines. The survey conducted randomly from March to August 2008.
There were 385 survey questionnaires distributed. A total samples 265 were collected and
after thorough inspection, it was then reduced down to 235 usable returns.
In Table 6.1, the descriptive statistics of the sample are summarized with percentages
enclosed in parenthesis. The average age of the participants obtained from the sample is
nearly 30 years old. The participants are comprised of 41% males and about 59% of the
participants are females. Approximately two-thirds of the participants were single in civil
status as of the survey was conducted. Forty-two percent of participated in the survey are the
teaching staffs while 58% are the non-teaching staffs. The average number of years working
obtained from the survey is 5 years. The distribution of samples with respect to the type
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Table 6.1 Descriptive statistics of the university worker participants in Metro Manila (235)
Age M = 29.23 years old, S.D. = 8.427 a
Gender
Male 92 (39.1%)
Female 143 (60.9%)
Civil Status
Single 156 (66.4%)
Married 79 (33.6%)
Occupation
University Staff 137 (58.3%)
Professor 98 (41.7%)
Number of years working M = 4.99, S.D. = 6.255 b
University type
State universities 156 (66.4%)
Private universities 79 (33.6%)
Educational attainment
Undergraduate 161(68.5%)
Graduate 74(31.5%)
Monthly income in PhP b
≤ 8500 69 (28.6%)
12500 74 (31.5%)
17500 35 (14.9%)
22500 28 (11.9%)
27500 15 (6.4%)
≥ 30000 14 (6.0%)
Household size M = 4.510, S.D. = 2.157 a
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Table 6.1 continued…
Location residence
Within Metro Manila 196(83.4%)
Outside metro Manila 39(16.6%)
Car ownership
Non 198 (84.3%)
1 34 (14.5%)
2 2 (.9 %)
3 1(.4%)
Average travel time from home to work place
By primary mode 33.2 minutes
By secondary mode 38.0 minutes
Average travel cost from home to work place 51.24 Php
Public transportation users 205 (87.23%)
Note: a M=mean, SD = Standard Deviation
b
Exchange currency (2008): 1Php is about 0.02USD
of the university (i.e. that is state and private universities): state universities have 156 (66%)
of the total participants answered the survey while 79 (34%) are the participants of the private
universities. Thirty percent of the participants have graduate degrees. More than one-third of
the participants receive an income of 12,500 PhP per month and only five percent receive a
monthly income of greater or equal to 30,000 PhP (approximately equal to 640 US dollars).
The collected sample has an average household size of 4.6 family members. Roughly, 80%
reside within Metro Manila. More than 80% of the participants do not own a private vehicle
during the survey was undertaken.
Average travel time using the primary mode is quicker making 33.2 minutes to travel from
home to work whereas using the secondary mode have an average of 38.0 minutes in order to
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reach the workplace from home. The primary mode is the usual mode used to travel from the
home to work place. Secondary mode is the alternative mode whenever the primary mode is
not available. The average travel cost from home to workplace is approximately 51.24Php
wherein approximately 87% of the participants were public transport users.
The frequency of information and communication technologies is shown in Table 6.2.
Cellular phones or widely known in short as cell phones are rapidly overtaking the landline
phones in the Philippines (Mendes et al., 2007). For this reason, the survey questionnaire
included the purpose of use of their cell phones: namely, work-related, personal, and hobby
and social. Nearly 50 percent of the participants said that they use cell phones for work
related matter one to four times a day and nearly one-third said that they use it greater than ten
times a day. As for personal use, two-thirds said that they use it more than ten times a day.
While for hobby and social purpose, approximately 40 percent said that they use it more than
ten times a day.
Other popular ICT gadgets and services were also included in the survey. For example,
Internet use per day approximately, 50 percent said that they use it 1-4 times a day while 30
percent said that the use it more than ten times a day. Communication using landline phones,
landline phones 40 percent use it 1-4 times a day and 40 percent use it more than 10 times a
day. The frequency of personal computers, about two-thirds said that they use it 1-4 times a
day and only 30 percent said that they use it more than ten times a day.
The list of social contacts in a social network for each participant was obtained from the name
generator data. As shown in Table 6.3, the average number of people for friends for
important matters in a person’s social network is approximately nine (9). The same is true for
friends for socializing roughly comprise of nine (9) people. The average number of people in
person’s social network for friends for advice is only five (5) while friends for small services
is three (3) having the least number of contacts in the four categories of friends. Summing up,
the average number of people in a person’s social network (for university workers in Metro
Manila) has an average of 25 persons. In Table 6.3, the percentage composition of each
category of friend in a social network reveals that the social network of the participants is
mostly composed of non-family members.
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Table 6.2 Frequency of information and communication technology (ICT) Use
Frequency of use per day
ICTs 1-4 5-9 >10
Cell phone use by purpose (per day)
Work-related 100 (47%) 50 (24%) 62 (29%)
Personal 35 (15.4%) 56 (24.7%) 136 (59.9%)
Hobby and social 70 (35%) 44 (22%) 85 (43%)
Other applications of ICT
Internet use 108 (53%) 37 (18%) 57 (28%)
Landline phone 78 (38.2%) 47 (23%) 79 (38.7%)
Personal computer 112 (56.9%) 32 (16.2%) 53 (26.9%)
Table 6.3 Social network descriptive dimension
Friends by categories Percentage (%) by
relationship Average number
of friends
family Non-family
Friends for important matters 15.84 84.16 8.8
Friends for socializing 3.23 96.77 8.6
Friends for advise 17.68 82.32 4.6
Friends for small services 17.20 82.80 2.9
In the overall social network, average number of friends 24.8
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6.4 Empirical analysis
6.4.1 Structural equation model analysis
Structural equation model (SEM) is a technique that can handle a large number of endogenous
and exogenous observed variables simultaneously (Joreskog and Sorbom, 1986). SEM has
been employed in studies such as travel behavior research (e.g. Golob, 2003;Ory and
Mokhtarian, 2009; Lu and Pas, 1999) and in telecommunications and travel demand
(e.g.Choo and Mokhtarian, 2007; Wang and Law, 2007). SEM has the ability to include latent
variables into the analysis. A latent variable is hypothesized and are not directly observable
that can only be estimated by means of measureable variables. The measureable or indicator
variables are a set of variables that is used to define the latent variable.
Every latent variable is associated to a set of observable indicator variables, which are assumed to be measured with error expressed as the following structural equation:
η = Βη + Γξ + ζ (eqn. 6.1)
Because a vector of latent variables η is unobservable, indicators are necessary to measure
them. Thus, the structural equation model is associated with two measurement models, as
follows:
𝑦 = Λyη + ε, (eqn. 6.2)
𝑥 = Λxξ + δ, (eqn. 6.3)
where:
B, Γ, Λy, Λx : unknown parameter array,
ξ : endogenous or latent dependent variable vector,
ζ, ε, δ : error term vector following a multivariable normal distribution,
x : vector of observed exogenous or independent variables,
y : vector of observed endogenous or dependent variables.
A structural equation model with latent variables can be perceived as a combination of two
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models: (1) a structural model and (2) a measurement model. The structural model signifies
the relationships among the latent variables across the path diagram while the measurement
model specifies the rules of correspondence between measurable variables and latent
variables.
One reason why structural equation model is widely used is that it presents a method for
clearly taking into account measurement error in the observed variables (to both dependent
and independent) a specified model. In addition to, SEM also allows researchers to easily
develop, estimate, and test complex multivariable models, as well as to study the direct,
indirect, and total effects among a set of variables (Mueller, 1996). Direct effects are the
effects that go directly from one variable to another variable. Indirect effects are the effects
between two variables that are mediated by one or more mediating variables that are often
referred to as an intervening variable(s).
6.4.2 Model specification
The following measures of constructs were developed, drawing from the conceptual model in
Figure 6.2. I attempted some variables to be included in the analysis but they exhibited less
significant for the reasonable model fit. Therefore, they were not included in the tabulation.
Social interaction: Social interaction involves the daily communication with family members,
friends, and colleagues or even with extended friends. The intensity of interaction depends on
who is being interacted which obviously reveals that interaction with close friends is in a
constant basis to nurture the friendship ties. Aside from whom they interacted with,
interaction also depends on what media they employ, it can be the traditional face-to-face
interaction or it can be technology-mediated interaction (e.g. cell phones, internet). In the
analysis, the attempt of including interactions like cell phone calls, landline calls, online chat,
sending emails as well as sending invites, letters or cards but then again they exhibit less
significant variables such that their means and standard deviations are not included in Table
6.4.
The number of text messages might be due to the feature of mobile phone in the Philippines
you can send messages to many people. Surprisingly, the frequency of sending text messages
in the Philippines might be due to cultural issue. In my personal knowledge of the
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Table 6.4 Latent variables and observed variables used in the model
Variables: Description Mean SD
Social interaction (ξ1 )
x : frequency sending text message to family members per day 1 18.45 19.41
x : frequency of sending text message to close friends per day 2 20.84 20.51
x : frequency of sending text message to colleagues per day 3 23.29 29.36
x : size of family members interacted face-to-face per day 3 22.92 25.24
x : size of close friends interacted face-to-face per day 5 23.27 29.20
x : size of colleagues interacted face-to-face per day 6 25.77 30.65
Social Network (η1 )
y : size of the social network 1 24.86 18.84
y : number of accompanying persons while shopping 2 1.97 1.07
y : number of accompanying persons when having dinner with friends 3 3.99 1.95
y : number of accompanying persons when visiting friends/relatives 4 3.26 1.71
y : number of persons when playing sports together 5 2.42 1.72
y : number of persons when attending in celebrations/parties together 6 3.67 1.90
y : number of persons when participating organization meetings 7 3.24 2.20
Social activities (η2 )
y : frequency of going to dinner with family per week 8 2.44 3.78
y : frequency of going to dinner with friends per week 9 2.80 1.71
y : frequency of going to shopping per week 10 2.79 1.81
y : frequency of visiting friends/relatives per week 11 2.29 2.76
Degree of Travel (η3 )
y : total travel cost per day 12 51.24 43.80
y : total trips traveled per day 13 3.81 1.52
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phenomenon of text messaging in the Philippines, most mobile phone companies have this
promo that when you buy a certain amount of load you can send unlimited messages to
friends with the same mobile phone network for 1, 3, or five days. The unlimited sending of
text messages depends on the amount of money load of the mobile phone. If the money load
is small, then the unlimited text service will expire in just a few days than with the bigger
amount of money load. And whenever they know that the unlimited text will expire in a few
hours, people usually take advantage of sending multiple messages to friends for as much as it
can send. This might be of the reasons why the Philippines is herald as the text-capital of the
world (Pertierra, 2002; Elwood-Clayton, 2005), which somewhat reflects the result of the
survey on the frequency of text messaging that we collected in 2007.
Social activities: The social activities included in estimating the structural equations were the
common events in our daily undertakings. The social activities enumerated have three out-of-
home discretional activities: dinner with family, dinner with friends, and visit friends and
relatives, and only one maintenance activity: shopping. Other social activities, like play
sports, watch movies in theaters or watch concerts, get involve in organization meetings and
functions, out-of-town vacation with families and friends and attend parties or celebrations
are also included in the analysis but were omitted to attain the best fit model.
Social network: Social network were obtained from asking the participants how many persons
they are usually with when they do a certain social activity. For example, when they go to
dinner with friends, how many of persons they usually go to dinner together? By the use of
name generator questionnaire, the participants of the survey were also asked to list down the
total number of friends in each category of friends enumerated. By performing this, the
participants are able classify their friends according to importance or in technical term that is
according to their strength of tie.
Degree of travel: The degree of travel included in the model comprise of only two variables:
total travel cost and total traveled trip. There are factors of degree of travel however for this
particular analysis I practically chose these two factors for the reason of availability of the
data sets.
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6.5 Model estimation results and discussion
The estimation result of the structural equation model was presented in Figure 6.3. Each of
the variables was modeled as a reflective indicator of its latent construct. In each
measurement equation, one of the coefficients is normalized to 1 for identification.
For measurement models to have sufficiently attained good model fit, appropriate indexes are
necessary for the evaluation of the best-fit model. The summary of the model fit results and
the suggested criteria of the indexes are also shown in Figure 6.3. The chi-square statistic
presents a test of the null hypothesis has the specified model structure that is the model fits
the data. The observed chi-square is 432.66 (d.f. = 148) with a of p < 0.001. This result
implies that the null hypothesis cannot be rejected. The suggested goodness of fit index (GFI)
should exceed 0.85 for the appropriate value GFI (Joreskog and Sorbom, 1986). The GFI for
the hypothesized model is exactly 0.85, which means the model is just within the marginal
acceptance level. The goodness of fit index adjusted for degrees of freedom (AGFI) should
exceed the suggested value of 0.80 (Cole, 1987). The AGFI of the model precisely gives a
value of 0.80, which means the fitted model is again just in the marginal acceptance level.
The results of both GFI and AGFI suggest a reasonably a good fitting of the data to the
hypothesized model.
The proposed model was also assessed against standardized root mean square residuals
(SRMR) as the supplementary goodness of fit indices. The SRMR was used for checking
model data fit because it results in lower probabilities of type I and type II errors when
compared to the root mean square error approximation and the Tucker–Lewis index in sample
sizes ≤250. Hu and Bentler (1999) proposed that values of SRMR <0.10 results in the least
sum of type I and type II error rates. Type I and type II error rates are used to describe
possible errors done in significance testing procedure. The estimated parameter, t-value and
the significance level of each variable are also shown in Figure 6.3. There are two paths at p-
value less than 0.005 and the other two paths are at p-value less than 0.01 in the part of the
structural model. Consequently, all of the paths in the structural model exhibit as statistically
significant and empirically supported the model.
Elaborating on the estimation result, the paths from social interaction (ξ1) to degree of travel
(η3) is mediated by social network (η1) and social activities (η2). From the hypothesis in
Figure 6.2, there are two paths can be observed from ξ1 to η3. The first path can be traced
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Figu
re 6
.3 T
he e
stim
atio
n re
sult
s of
the
stru
ctur
al e
quat
ion
mod
elin
g
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from ξ1 to η1 then to η2 and finally to η3. The second path is from ξ1 directly to η2 and lastly
to η3.
The path from social interaction to social activities, gives also a positive and significant
estimated parameter of 0.14 (t-value of 2.55). The path from social interaction to social
network, gives a strong positive and significant estimated parameter 0.51 (t-value of 2.11).
Based on the results of the structural model, social interaction posses the causal effect to
social network, in which it expands the network into a bigger composition. The key reason to
this could be is simply to keep in touch with friends to preserve the network of friendship. In
the same manner, social interaction also has the strong and direct causal influence to social
activities. The main reason of this could be pointed out on the idea that in some ways social
interaction plays a vital function as a “motivator” in the formation of social activities
(Carrasco and Miller, 2006).
The hypothesis, which is the path from social activities to the degree of travel, provides
a positive and significant estimated parameter of 0.29 (t-value of 3.12). The path from social
network to social activities, gives also a positive and significant estimated parameter of 0.07
(t-value of 2.49).
The indirect positive and significant effect of social interaction to social activities can also be
observed via social network. Social activities reflect as having a strong positive and
significant direct effect on the degree of travel. Also, an important result of this study is the
path from social interaction to degree of travel passing through social activities produces a
substantial effect to the degree of travel. The degree of travel considered in this study is the
total cost of travel per day and the total trips traveled per day.
The key findings of the structural model indicate that social factors could be the essential lead
to understand why and by how much people travel. Social interaction shapes social network
and creates social activities. Wherein, social network also could yield to develop social
activities. Consequently, social activities take the command to generate travel. This concept
was not yet explored in the realm of transportation planning policies. However, the result in
the structural model evidently implies that there is significant effect of social factors to travel;
it may be a direct or indirect effect.
Primarily, this study provides initial step towards understanding and interlinking social factors
to travel in the context of the university workers of Metro Manila, the findings indicate that
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social factors can be used as the determining factor for the degree of travel, which this aspect
was not explored yet particularly in the developing countries.
Apart from the proposed model presented in Figure 6.3, other alternative models were also
performed (see Appendix 1 for alternative models) to determine which model that exhibit best
goodness of fit. After all, the model shown in the thesis was supported among the alternatives
because:
1. Social activity has significant effect on travel while the other three analyses
show no significance for social activity to travel behavior.
2. The goodness of fit of the proposed model is better than the alternative models.
3. Other factor becomes less significant when the causal relationships of social
interaction and social network to travel behaviors are added.
The first alternative model, however, give no significant t-value for the causal effect of social
activity to travel behavior. By comparing this model to the proposed model, there is a big
difference of the t-value for the causal effect of social activities. The reason for this might be
due to the following:
1. Multicollinearity between social interaction and travel behaviors as well as
variables between social network and travel behavior.
2. Social interaction and social network are highly correlated with travel behavior.
For social interaction, high frequency of text messaging might one of the reasons
of high correlation with travel behavior. For social network, large set of friends
might also be the reason of high correlation with the travel behavior.
Since one of the reasons of selecting the proposed model compared to the first alternative
mode model is multicollinearity between social interaction and travel behavior as well as
social network and travel behavior, the model structure is performed by making social
network as the intermediate variable. However, the direct causal relationship of social
network to travel behavior makes the t-values of some variables insignificant. And by testing
the model structure social interaction as the intermediate variable, again, resulted to
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insignificant t-values for some variables. Hence, the proposed model performs the best model
fit than the alternative models.
6.6 Synthesis
This chapter has verified the hypothesis that social factors, such as social interaction, social
activities and social network, would have a significant effect on travel factors, i.e. total travel
cost per day as well as the total traveled trip per day as considered in the study. The result of
the structural model using the survey data collected from university workers in Metro Manila,
the Philippines, indicates statistically positive and significant in all estimated parameters.
Therefore, it supports and confirms the hypothesis developed in this particular study.
From the perspective of the university workers within Metro Manila, the structural model
reveals that social interaction has a substantial causal effect on social network as well as on
social activities. Moreover, social network could be a causal factor to social activities. There
is also a significant effect of social activities to the degree of travel. In addition, as depicted
in Figure 6.3, the strong significant effect comes from the path of social interaction via social
activities then finally to the degree of travel. The primary reason for this might be that social
interaction would act as the stimulating factor to create or initiate social activities. Social
activities need a dynamic movement that in some cases probably would oblige the need to
make a trip. Although, it would not be sufficient to say that these are the only factors that
affect travel behavior. There are other also factors that affect travel however they are beyond
the discussion of this particular hence they are not dealt in here.
These results, though, they are the outcome only for a small population (university workers
Metro Manila, the Philippines), may imply that the inclusion of social factors would be
treated also with importance in the future development of transport planning. Most notably,
the importance to realize the inclusion might be part of the consideration of transport policies,
even in the developing countries.
One of the limitations of the study in this chapter is the consideration of the group of
population that could be augmented into a bigger set of general population in the future
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endeavors of the study. Hence, a broader survey for general population is recommended for
the future works. Although the findings of the current study is enriching and useful, there are
also other fertile areas that need to explore more in detail especially on the interactions made
by ICT applications. Since, these new technologies are built purposely for social interaction
nowadays however its potential effects have not fully explored yet in the field of
transportation planning.
In like manner, transport policies have not yet include the concept of social factors especially
on social interaction and social network in forecasting travel. Although, there are preliminary
initiatives, recently done, to examine these social dimensions and relate it to travel behavior.
The research study is also considering how travel is affected by the capability of ICTs to
reduce the planning time horizon of some social activities, which is the main subject matter to
be tackled in Chapter 7. One avenue for future research also is to explore on including the
spatial distance of the friends in the social network subject, which is actually the current state
of the research progress.
***
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CHAPTER 7
THE EFFECTS OF ICT USE ON TIME PLANNING AND
SOCIAL DIMENSIONS TO TRAVEL BEHAVIOR
7.1 Introduction
The emergence of modern lifestyles has included dramatic changes in areas. The advent of
information, communication and technologies (ICT) has permeated the daily life of many
people in so many ways. For instance, the utilization of ICT enables us to do more social
activities in constant coordination. Due to constant coordination by ICT, peoples’ decision
on time planning to engage in social activities might also be affected.
Time planning is defined here as the span of time during which the traveler’s decision is
made before engaging in an activity. For example, if a person is invited by his friend to visit
his friends, he has to decide and tell his friend whether he will visit or not. The time the
person receives and information until the time he decides is the so-called time planning.
The importance of time planning in transportation is that peoples’ decision on time planning
might affect travel patterns. For instance, a call from mobile phone is used for instant task
(time planning is instant) that on certain occasion obliges to make an unplanned trip.
Moreover, with the wider diffusion of ICT, social networks expand because of the ability to
nurture networks that are more complex. Furthermore, travel might be either induced,
reduced, or substituted by the use of ICT.
When ICT was insufficiently diffused, transport studies mainly analyzed the relationship of
socio-demographic characteristics to the social activity participation and subsequently to
travel. For example, Lu and Pas (1999) applied the structural equation model (SEM) to better
capture and understand travel behavior. They revealed that travel behavior can be better
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explained by including the aspect of activity participation. They also found that a significant
relationship between in-home and out-of-home activity participation exists. Aside from
social participation, an indirect effect of socio-demographic characteristics on travel behavior
was also investigated in their study.
Moreover, according to Gershuny (2003), the indicator of social change is not only the mere
change in time-use patterns but it is also the meaning of socio-economic development.
Socio-economic development is the process in which the provision for basic wants allows to
shift time gradually towards production (work) and consumption (or leisure) activities
relating to wants that are sophisticated.
A very simple graphical representation of this change of allocation of time resources is
illustrated in Figure 7.1 as suggested by Gershuny (2000). It is represented as a form of the
box which specifies the division of the adult total time, expressed in 1,440 minutes. The
vertical columns are divided in proportion to the time spent in the types of activities (work
and leisure classifications). The width of the middle column in the box represents the
aggregate of the formal and informal work and consumption (leisure) time. The box is
divided horizontally into series of columns which represent the purposes of activities.
By comparing the adult time use in the 1780s in (a) to the 1980s in (b), it is clearly
demonstrated the horizontal shifts over the centuries, in which columns representing the more
‘basic’ wants become relatively thinner in 1980s. And those representing more ‘sophisticated’
wants become thicker in terms of adult time. Vertical shifts are also depicted in which the
mixture of activities associated with each purpose changes – a larger portion of informal
work to formal work; more pronounced increase of consumption (or leisure) time.
The nature of socio-economic development appears clearly from this picture more so now
that the growth of high technologies is apparent, which would likewise mean modification of
activities would likely to occur. The nascent of high technologies has been ubiquitously
essential and inseparable to the daily activities due to its extensive application that changes
people’s lifestyle, new possibilities of travel patterns are produced, which did not necessarily
exist before ICT takes place.
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Figure 7.1 Adults’ time use over two centuries (Adapted from Gershuny, 2000)
40
100
60
80
20
0540 1260720 1080900 1440180 360
Minutes per day
40
100
60
80
20
0540 1260720 1080900 1440180 360
Minutes per day
(a)
(b)
Agriculture, menial services
Manufacture, sophisticated services
Paid contracted work
Informal work
Consumption/leisure
Agriculture, menial services
Manufacture, sophisticated services
‘The Economy’
Informal work
Consumption/leisure
1780s (estimated)
1980s (UK data for 1984)
Sleep Food Other needs
Sleep Food Other needs
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In the study by Hjorthol (2008) where ICT use like mobile phone was associated to travel as
well as planning time. This study revealed that there is a positive correlation of short
planning time and the use of mobile phone exists. Nowadays, time planning might be a
significant factor that might affect travel due to the fact that ICT, say, mobile phone in
particular, can be used as a tool for instant task. For example, while on travel, a house
member might call and asks for a favor which in some certain occasions obliges to make trips.
In this scenario, as time planning horizon to do the favor would be instant and the travel
pattern changes, another trip might be added to the usual trip. Because of ICT use, therefore,
time planning might play an important role in transport and it may produce new possibilities
of travel patterns.
With the aforementioned studies, it is the motivation of this study to further extending the
previously suggested concept by Lu and Pas (1999) by including the relationship of ICT use
to time planning and to social network in the analysis of travel behavior. The study
speculates that ICT use would change the travel behavior and the patterns of social activity
participation.
The objective of this chapter is to analyze the relationship among the ICT use and its effects
on time planning, social activity participation, and social network of the respondents on the
patterns of travel behavior.
7.2 Hypotheses
The proposed hypothesis of the study, as shown in Figure 7.2, demonstrates the conceptual
structure of ICT use, social network, time planning, social activities and travel. There are
four factors considered as determinants of travel behavior in this particular study. These are
(1) ICT use, (2) social network, (3) time planning, and (4) social activities. As utterly
mentioned in the previous chapters, there might be some other factors affecting the patterns
of travel behavior however for the purpose of this study we would like to focus the
determinants aforementioned above.
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Figure 7.2 Hypothesis of the effects of ICT use
There are six relevant hypotheses in this chapter:
First, travel behavior is assumed to have a significant relationship with social activity
participation as was previously suggested by Lu and Pas (1999) that is also supported by the
gathered results of Chapter 6 of this thesis with respect to the study of the university workers
in Metro Manila.
Social activityparticipation
Time planning
Social network
Travel behaviour
ICT use
Socio-demographic characteristics
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Second, it is also assumed that social network might have direct and significant effect on
travel as well as on social activity participation. Based on the hypothesis of Axhausen (2003),
a person’s travel behavior is shaped by his social network. It simply means that the bigger
the size of social network is, the more likely a person will make trips. Moreover, if a person
has a large group of friends he or she is more likely to participate in social gatherings or
activities.
Third, as for the time planning, it is assumed that it has some effect of social activity
participation. The shorter time planning is made or decided the more chances of
accommodating or engaging in some social activities. Hence, time planning is presumed to
have a significant and negative effect on social activity participation. This holds true for
travel, the more trips are made the shorter the span of time planning to have negative effect
on travel. For example, when a person makes time planning shorter, he has the tendency to
make more trips because has already made his decision for an activity so he can have more
time to make some more trips.
Fourth, due to being ubiquitous, ICT use is assumed to have an effect on time planning of
social activities. This is because of its nature to be accessible and readily available at all
places all the time. The more ICT is used the shorter the span of time planning to decide
whether to participate or engage in a social activity or not.
Fifth, again, as ICT being a tool used most of the occasion, it presumed that it affects the
degree of participating in social activities as well as the structure of the social network. In
other words, the more ICT is used the more a person is likely to participate in social activities.
The same is true for social network, the more ICT is used the more a social network is likely
to expand.
Lastly, ICT use is presumed to have direct and significant effect on time planning, on the size
of social network as well as on the frequency of social activity participation. The reason
behind of the assumption that ICT use would have direct effect on time planning is that ICT
has been so convenient and efficient to use in everyday organization of activities and this
may even hold true across and even in countries with less ICT penetration.
Originally, the inclusion of socio-demographic characteristics (represented in dashed lines as
well as the arrow) was attempted to be included in the hypothesis anticipating that somehow
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it has some effects on ICT use and travel behaviour. However, it has been finally omitted for
the reason that most of the respondents are university students and workers which would have
similar socio-demographic characteristics that might not exhibit any significance in the model.
7.3 Data and analysis
To verify the hypothesis, a survey was conducted in the universities within Metro Manila,
Philippines in 2007. The targeted respondents of the survey are initially the participants from
pre-selected universities, both state and private universities explained in Chapters 5 and
6. There are 522 respondents gathered who are either university students, office staffs or
professors.
The survey questionnaires contain two parts: (1) main questionnaire and (2) name generator
(Padayhag and Fukuda, 2010). The main questionnaire is intended to capture the patterns of
social activities, the patterns of ICT use, as well as the time planning of activities by the
respondents while the name generator elicits the number of social contacts, the relationships
of social contacts to the respondent and the approximate distance of the social contacts.
As presented in Table 7.1, the average age of the respondents is about 24 years old and 46%
of them are males. Mostly, the respondents are single which comprise of about 85%. The
average of household size is about 3.18. Most of the respondents are living within Metro
Manila and has an average number of years of stay in the present location of 7.22 years.
Only 19% of the respondents own a car. From the name generator questionnaire, the average
number of friends a respondent usually have is roughly 24. This is the sum of all categories
of friends shown in Table 7.1, where in the case of the Philippines, the largest group of
friends is the friend for important matters (an average of 8.64 number of friends). The least
number of friends are found to be the friends for small matters, which I think is analogous
that we have only few group of friends for whom we can ask small matters.
From the main questionnaire, the information on the frequency of ICT use, time planning,
social activity participation, and travel behaviour was extracted. As shown in Table 7.2, the
latent variables are ICT use, time planning, social activity participation, social network, travel
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Table 7.1 Categories and average number of friends
Categories of Friends Average number of
friends
Friends for important matters 8.64
Friends for socialization 8.63
Friends for advice 4.64
Friends for small matters 2.92
Table 7.2 Descriptive result of university workers and students with N = 522
Socio-demographic characteristics
Age 24.13 (M), 7.32 (SD)
Sex (female) 226 (46.3%)
Civil status (married) 78 (14.9%)
Household size 3.18 (M), 1.92 (SD)
Residence location (within Metro Manila) 415 (79.5%)
Number of years of present location 7.22 (M), 8.78 (SD)
Car ownership
None 426 (81.6%)
1 77 (14.8%)
2+ 19 (3.7%)
Number of cell phone owned
1 362 (69.3%)
2+ 160 (30.7)
Social network 24.05 (M), 15.93 (SD)
M: mean SD: standard deviation
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behaviour. The observed variables were also enumerated with their corresponding mean and
standard deviation. Although, there are several observed variables included in the
questionnaire but only those variables that exhibit the best fit are included and enumerated
here for brevity.
The ICT use in this particular study is on the use of text messaging, cell phone calls and
landline calls only. Internet use, email or chat, is opted not to include in the analysis since
then it was decided to focus only on the mobile phones and landline phones as the variable
for ICT use. However, internet use maybe considered and employed as ICT use in the future
work of this research.
As aforementioned, time planning is defined in this study as the planning duration of decision
before engaging or performing a social activity. Each social activity enumerated has a
corresponding space provided for the respondents to fill in the span of time needed in order
for them to make a decision to engage in the activity or make a travel.
As for the social activity enumerated in this study, all of the social activities employed are the
common out-of-home activities and is also based on the local context in the Philippines; for
example, shopping, visit friends and out-of-home dinner with friends. The in-home activities
such as doing household chores or watching TV at home are unintentionally not included in
the survey questionnaire; however, for future work this may be included as well.
The analysis make use of the structural equation model (SEM) which is a method of analysis
that can deal with several endogenous and exogenous observed variables simultaneously
(Joreskog and Sorbom, 1986).
The observed variables are a set of variables that is used to define the latent variable. Every
latent variable is associated to a set of observed variables, which are assumed to be measured
with error expressed as the following structural equation:
η = Βη+ Γξ + ζ. (eqn. 7.1)
Since a vector of latent variables η is unobservable, indicators are necessary to measure them.
Consequently, the structural equation model is related with two measurement models,
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Table 7.3 Latent and observed variables used in the analysis
Latent variables Observed variables Mean SD
ICT Use (per day)
Cell phone call per day 5.44 8.87
Text messaging per day 34.75 40.92
Landline call per day 6.00 13.70
Time Planning (minutes)
Time plan to have dinner with friends 68.60 141.85
Time plan to attend organization meetings 128.77 175.34
Time plan to shop 36.04 88.84
Time plan to watch movies 60.84 121.26
Time plan to visit families and friends 210.48 525.10
Social activity participation (per week)
Organization meetings 2.40 2.32
Visiting of families and friends 3.20 1.62
Shopping 2.71 2.19
Dinner with friends 3.62 1.97
Watch movies 1.97 1.70
Play sports 2.10 2.21
Social network
Num of accompanying persons for shopping 2.49 1.42
Num of accompanying persons while visiting families/ friends
2.98 1.49
Number of accompanying persons for attending celebrations
4.24 1.77
Size of social network 24.05 15.93
Number of accompanying persons for dinner with friends
4.02 1.73
Travel dimension
Trip frequency 3.40 2.20
Trip cost (Philippine peso, Php) 45.94 44.24
Note: 1 Php = 0.02USD
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are as follows:
y = Λy
x = Λ
η + ε, (eqn. 7.2)
x
All parameters and variables included in these equations are summarized as:
ξ + δ, (eqn. 7.3)
B, Γ, Λy, Λx : unknown parameter array, ξ : latent dependent variable vector,
ζ,ε ,δ : error term vector following a multivariable normal distribution,
x : vector of observed exogenous or independent variables,
y : vector of observed endogenous or dependent variables.
7.4 Results and discussions
The SEM has been applied for empirical testing of the hypothesis. For measurement models
to have acceptably achieved good model fit, appropriate indexes are essential for the
estimation of the best fit model.
The summary of the model fit results and the suggested criteria of the indexes are shown in
Figure 7.2. The chi-square statistic presents a test of the null hypothesis that the specified
model structure does not fit the data. The observed chi-square is 688.19 (d.f. = 181) with a of
p < 0.001. This result implies that the null hypothesis cannot be rejected. The suggested
goodness of fit index (GFI) is expected to exceed 0.85 for the appropriate value (Joreskog
and Sorbom, 1986). The GFI for the hypothesized model is exactly 0.89, which means the
model is at the acceptance level. The goodness of fit index adjusted for degrees of freedom
(AGFI) is expected to exceed the suggested value of 0.80 (Cole, 1987). The AGFI for the
hypothesized model is 0.86, which means that the model is evidently at the acceptance level.
Therefore, the results of both GFI and AGFI suggest a reasonably a good fitting of the data to
the hypothesized model.
Based on the estimation results of the standardized coefficients of a structural model, as
presented in Figure 7.3, it is found that ICT use strongly might affect the structure of social
network, meaning, the more frequent use of ICT the bigger the size of social network will
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Figu
re 7
.3 T
he e
stim
atio
n re
sult
of
the
stru
ctur
al e
quat
ion
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Table 7.4 Measurement variables, standardized parameter estimates and the t-values
become. In this case, we have presumed that using ICT makes wider social network however
it can also be the other way around like having large social network makes a person to ICT
more. In spite of the possibility that having a bigger social network will cause the frequency
Latent variables Measurement variables Standar-dized
t -values
Social network Num. of accompanying persons for shopping
0.858 NA
Num. of accompanying persons while visiting families/ friends
5.342 1.312
Num. of accompanying persons for attending celebrations
1.240 5.637
Num. of accompanying persons for dinner with friends
1.509 5.577
Num. of friends from the name generator 0.543 3.315
Social activity Frequency of Attend parties 0.802 NA
Frequency of Shopping 0.848 5.972
Frequency of Sports 0.939 6.434
Frequency of Visit friends 1.305 7.134
ICT use Number of Cell phone call 0.649 NA
Number of Text messaging 0.422 3.650
Number of Landline call 1.140 5.948
Time planning Time to plan to have dinner with friends 0.658 NA
Time to plan to watch movies/concerts 1.099 6.866
Time to plan to attend organization meetings
0.467 4.584
Time to plan to shop 0.847 6.370
Time to plan to visit families and friends 0.268 2.791
Travel behavior Total number of trips 0.930 NA
Total cost of trips 0.506 2.187
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of ICT use might also exists; however, in the case of the Philippines and as also noted by
Pertierra (2005) that it is one of the characteristics of the Filipinos that owning ICT a mobile
phone, in particular, is also understood to making more friends or expanding his social
network.
The measurement variables used in the SEM analysis and its standardized estimates are
presented in Table 7.4 with t-values. In addition, in each of the measurement equation, one
of the coefficients is assigned to be normalized, which has NA t-values, for the sole reason of
identification.
ICT use positively affects time planning, that is, it does not necessarily reduce the time for
activity/travel consideration). It simply means that ICT use affects on how the participation
of social activities is being planned. For example, when a person makes a plan to participate
in an activity and he uses ICT more often he has the tendency to extend his decision to a
longer time since he can easily make a contact to his friends, if he will join or not, through
ICT. This means that ICT loosens the time constraints of deciding social participation. In
addition, Linkov et al. (2008) stated that technology, regardless of its sophistication, cannot
make judgment calls or generate creativity as this capacity is uniquely human, it can only
enhance communication and more efficiently process information. This in contrast to the
hypothesis previously mentioned.
The negative sign between time planning and social activity means that when time planning
is made short, it tends to make more social activity participation. This is because when time
planning is shortened, respondents would tend to accommodate unplanned additional social
activities.
For example, when a person shortens time planning to participate in social activities the more
likely he is going to participate social activities depicted in Figure 7.3 by the arrow from time
planning to social activity participation, which has negatively significant effect. For instance,
when the time planning to visit friends is shortened – meaning, the time allotted to make a
decision to visit friends is quick – it might tend to make a person participate in more social
activities. The reason of this might be because a person will have the tendency to
accommodate additional and prioritize activities caused by short time planning, which could
be done in another time or day. This corresponds to the suggestions of Golob (2000) that cell
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phones, other portable computers, and communication devices have redefined our ability to
plan or conduct business and dynamically schedule activities.
In addition, ICT use tends to positively affect social activity, meaning, the frequent use of
ICT like mobile phone would likely to engage in participating social activities. The main
reason for this might because ICT applications are able to widely disseminate encouraging
information of social events or any kind of event where those avid users of ICT are as you
would expect attracted to participate in those social events. This result conforms to the study
of Claisse and Rowe (1993) in which telephone generally plays a complementary or neutral
role on social activities.
It is also found that the composition of social networks has positively significant effects on
the participation of social activities: the respondent who has wider and larger batch of friends
has higher possibility of participation in social activities. Though, I have collected the social
network of the respondents and categorized them according to the strength of tie or closeness,
in the term of layman, but this categorization found to have no significant effect on travel.
Changes in patterns of leisure participation arise from cultural, social, economic and
environmental influences, such as changes in social values, personal incomes or technology
(Cushman et al., 2005).
Furthermore, the result shows that the social network factor is most likely to have positive
effects on travel behavior. This would mean that the more friends a respondent have the
more he is likely to make trips, which is not necessarily social trips and it can also be a work
trip. This result conforms to the studies of Carrasco and Miller (2006), Urry (2003) and
Larsen, Urry, and Axhausen, (2006) that the social network of a person somehow shapes the
pattern of his travel behavior. This result is also backed up by results of the previous
chapters, especially chapters 5 and 6 of this thesis.
Lastly, social activity participation has a direct positive and significant effect on travel
behaviour. It means that the more social activity participation the respondent makes or
accommodates the higher the probability he is going to make travel. This result confirms the
result revealed by Lu and Pas (1999) that social activity participation is essential to analyze
travel behavior. The implication of this result to understanding travel behaviour is that, as
ICT use affects time planning time planning affects the reorganization of activities. Some
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activities might be done at instant with shorter time planning which might produce new
possibilities of new travel patterns.
Other structural models are also performed to check on which model structure give the best
model fit (See appendix 2). However, among the structural models presented the proposed
model likely to be present the best model fit since the alternative models have reduced
goodness of fit index at the same time the t-values of some latent variables exhibited
insignificantly.
7.5 Synthesis
This chapter has primarily examined the relationship among variables like ICT use, time
planning, social activity participation, social network and travel behaviour. By using the
structural equation model analysis (SEM), the result has demonstrated that there is a
significant relationship among them.
The empirical result using the data from university students and workers of universities in the
Metro Manila, Philippines revealed that there might be a positive and significant effect of
ICT use to social network. In similar, ICT use also significantly might affect the time
planning horizon, which means that the frequent use of ICT by the respondents would not
necessarily mean reduction of the time planning horizon consideration of social activities or
possibly travel. The empirical results indicated that it would be important to take notice on
the influx of ICT and the roles it portrays, most particularly on time planning – since it might
have some capabilities to organize and reorganize activities (e.g. shorter time planning
enables to accommodate more social activity participation), on the frequency or patterns of
social activity participation and travel behaviour.
Another important to note throughout this study is the result of the structural model that time
planning would negatively affect social activity participation, in other words, the shorter time
planning is the more social activities would be accommodated or participated. For instance,
when social activity like visit with friends is planned at shorter time, it might tend to
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accommodate or to participate more social activities since an activity, like visit to friends, has
already been decided and tends to reorganize to accommodate other set of activities.
In general, the result implies that ICT use affects the time planning horizon for which time
planning would negatively affect the social activity participation. This would mean that
shorter time might tend to increase social activity participation, which may also entail new
possibilities of travel patterns simply because of ICT use has the capabilities that it loosens
time and spatial constraints.
There are two limitations encountered as I go along this research study. First, the data did not
furnish the set of in-home activities but given the opportunity and for further research
endeavors, it is hoped to include this as well. Second and lastly, the result only represents the
university workers and students in Metro Manila. And, hence, I would say that the results
that I gathered in this study, in terms of the richness of the respondents interviewed, are not
substantial. The results may not be a conclusive outcome since it is not represented by the
general public. These limitations are subject for future studies and hopefully resolve these
issues.
***
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CHAPTER 8 CONCLUSION AND FUTURE RECOMMENDATIONS
Information and communication technology has affected people’s lifestyle, may it be in work,
leisure or other significant activities. For example, the ways of communicating and
socializing has tremendously changed from the past decades. The effects of these are
summarized in this chapter based on empirical findings through this thesis. I put all together
the vital issues and conclusions, derived from this study, cite the possible areas of application
of this study. Most importantly, the implications and applications of the LATS data results to
the results of Metro Manila of are emphasized in the succeeding sections of this chapter. And
finally, recommend potential topics for further research.
8.1 Summary and conclusions
This study has addressed some empirical issues in conceptualizing travel behavior analysis
that incorporates the attributes of ICT use and social dimension. The general objective of this
study was to construct the conceptual framework of travel behavior. This is done by
investigating and incorporating the impact of ICT use and its effects on social dimension.
The conceptual frameworks proposed in this study were performed in two cases, that is, the
case of developed and developing countries. In the case of developed countries, city of
London is the chosen representative as argued in Chapters 3 and 4 while the case of the
developing countries is represented by the data collected in Metro Manila as in Chapter 5, 6
and 7. True to some studies, there are some inevitable limitations encountered along the
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process of analyzing the dataset. In spite of these, this study was able to establish and
construct empirical models that incorporate the impacts of ICT and social dimension to travel
behavior.
At the initial stage of the analysis, which is in Chapters 3 and 4, I investigated the effects of
ICT in case of developed countries, represented by the city of London. In these two chapters,
I demonstrated how ICT affects the frequency of trips and tour complexity. Most
importantly, I defined and classified telecommuting in terms of numbers of hours of usage.
In the succeeding three chapters, which is Chapters 5, 6, and 7, ICT use in the case of
developing countries (Metro Manila, Philippines) were dealt with the inclusion of social
dimension. The following paragraphs will discuss the empirical models developed in this
dissertation.
First, I investigated ICT in the context of a developed country and in the scenario where
mobile phone possession is only significant to those who are working and with income. This
is the main reason on taking only the working population as the sample for the analysis.
Another reason is that the analysis purposely performed to take a quick look back of how ICT
formerly affects travel behavior. I employed LATS 2001 data and considered mobile phone
possession and telecommuting as the ICT applications that affect travel behavior. Initially, I
examined the ICT effects on the frequency on the basis of weekday trips of Londoners. The
results supported our expectations that mobile phone possession tends to increase trip
making.
Secondly, I established the definition of telecommuting and classified it by the amount of
time use that causes the shift of travel patterns. The results confirm that telecommuting
affects total trips. The ordered regression analysis suggests that those telecommuting much,
make less trips per day. The decrease of the total trip is however much less than the reduction
in work trips confirming the in the literature well described substitution effects of
telecommuting. The analysis confirms that these substitutions are likely to be leisure and
shopping trips. To manage the trip substitution effects of telecommuting hence a careful
design of neighborhoods might be of increasing importance. Those nearby “corner shops”
and cafes within local shopping streets could be profiting from telecommuting trends since
they offer possibility for additional spontaneous trips arranged; for example, by mobile
phone. The inclusion of some geographic characteristics in the analysis gives some support
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for such a conclusion.
Thirdly, aside from the frequency of trips, I is also investigated the possible effects of ICT on
tour numbers and tour complexity. The study established and classified the types of tours.
There are eight (8) types of tours employed in the analysis. As for the number of tours,
mobile phone possession tends to increase the number of home-to-home tours per day. I
employed the ordered probit regression in order to investigate the effects of ICT on tour
complexity. It is found out that those who do a small to medium amount of telecommuting
tend to make more complex tours and it has almost the same number of tours compared to
those who do not telecommute at all. Only for those telecommuting a lot we can find the
hypothesized effects of more simple home-to-home tours.
This study found that the amount of time of telecommuting plays a significant role to identify
the cause of shift of travel behavior. In this case, those full time workers who do much
telecommuting indicate that reduces tour complexity and that they entangle it into several
simple tours. Both full time workers who do not telecommute and full time workers who do
some telecommuting have an increasing effect on tour complexity. Likewise, with part time
workers, only those who do much telecommuting that do not exhibit any significance to the
model. With these results of the analysis, one might speculate that the entangled simple tours
are tours to the “café shop” or to the gym in order to escape from isolation and from sitting in
front of the computer all day.
As aforementioned in the preceding paragraph, the results from the LATS 2001 data posed
some insights for the case of the developing countries. Since most of the studies regarding
ICT are carried out from the developed countries, this indicates that it is a good intention to
study the perspective of the developing countries, represented by the data taken in Metro
Manila. Hence, in the fourth step, I considered to investigate ICT effects in the case of the
developing countries. This time, I incorporated the concept of socialization and travel
behavior, measured by the frequency of side-trips made while returning home after university
classes. The incorporation of social dimension in the travel behavior analysis would make it
more meaningful and sound empirical model to estimate travel demand, nowadays. It is
found that certain types of socialization have significant effects on trip frequencies among the
university students in Metro Manila. By taking the number of side-trips made while heading
home as the dependent variable in the SEM (Structural Equation Modeling) analysis, direct
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and positive effects were found for the number of people with whom one interacts face to
face per day, the frequency of text messaging, and the size of social networks. The results of
this study also may imply that technologically mediated forms of communication (e.g., text
messaging, online chatting) are modes of socialization employed by university students,
although online chatting by itself does not appear to contribute to the generation of trips.
From the viewpoint of Metro Manila, text messaging serves a vital role in daily undertakings
and it is not only inexpensive, but also convenient to use. The daily activities of individuals,
it may be in personal or in other matters, have become closely tied to the culture of sending
text messages.
Fifth, I hypothesized that social factors; such as social interaction, social activities and social
network, would have a significant effect on travel factors. From the perspective of the
university workers within Metro Manila, the structural model reveals that social interaction
has a substantial causal effect on social network as well as on social activities. Moreover,
social network could be a causal factor to social activities. There is also a significant effect
of social activities to the degree of travel. This makes social activities as an important
intermediate indicator in the conceptual framework. In addition, the strong significant effect
comes from the path of social interaction via social activities then finally to the degree of
travel. The primary reason for this is that social interaction acts as stimulating factor to form
social activities. More so, because social activities need a dynamic movement that in some
cases probably would necessitate for travel.
Sixth, I examined the relationship among ICT use, time planning, social activity participation,
social network and travel behavior. The result demonstrated that there is a significant
relationship among them. The empirical result using the data from university students and
workers of universities in the Metro Manila, Philippines revealed that there might be a
positive and significant effect of ICT use to social network. Similarly, ICT use also
significantly might affect the time planning horizon, which means that the frequent use of
ICT would likely to make longer time planning horizon and does not necessarily reduces it.
The empirical results also indicated that it is important to take notice on the influx of ICT and
the roles it portrays particularly on time planning, where it is capable to organize and
reorganize activities, on the patterns of social activity participation and on travel
behavior. Another important to note throughout this study is that time planning negatively
would affect social activity participation- meaning, the most common social activities only
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requires shorter time planning. For instance, social activity like dinner with friends are
usually planned at an instant are more frequent than social activity like going to parties,
which are merely on occasions only and are planned ahead for a longer time. In general, the
result implies that ICT use affects the time planning horizon in which time planning also
would affect the social activity participation. As time planning affects social activity
participation, it might mean that it will lead to provide new possibilities of travel behavior
patterns.
The results of London gives an interesting manifestation of ICT that it significantly affects
trips, tour numbers and tour complexity. Even though the data is collected in 2001 and ICT
then was still in the early stage of growth, the results imparted a significant implication
especially to the developing countries that are currently adopting the development of ICT.
The experience of ICT in London in 2001 may be considered as a good lesson for the future
improvement of transport planning policies of the developing countries. For example, the
type of trips that those with mobile phones usually do poses a good insight in order to
examine if it has some similarities with the case of the developing countries.
The results in Metro Manila using the 2007 data collected with respect to ICT use
corresponds to the result of ICT that affects travel for London data, despite of some
limitations and minor disparities encountered. Moreover, the analysis for Metro Manila has
incorporated some part of social dimension namely: social interaction, social activities and
social network. It has also conceptualized as having an interrelationship with travel behavior
and ICT use.
Both results from London and Metro Manila provide insights to other countries with their
own perspective of ICT use. Especially now that there are more developing countries are
aggressively embracing the development of ICT, particularly, on mobile phone due to its less
expensive acquisition cost, convenient and handy to use. In addition, the result of Metro that
dealt social dimension affecting travel behavior might also give a useful insight to future
development of transport planning policies. The incorporation of social dimension might
give more practical meaning in the travel behavior analysis especially that the growth of ICT,
in terms of penetration rate, is coupled with an increasing usage in social dimension.
Although, ICT has prematurely been foreseen to substitute or reduce travel but based from
172
the results of this study as evidence that the overall reduction in travel is a very unlikely
outcome. Instead, travel is not necessary eliminated but rather it merely changes its purpose.
This is because the goal of reduction of total travel negotiates among other several social
goals, which may also dependent on the increase of variety and sophisticated nature of human
interactions.
Furthermore, the implication of this study to transportation planning policies is on the policy
of reducing or eliminating the incidence of unnecessary trips which is a practical approach for
the sake of energy conservation and other environment-related issues. And, in order to attain
the targeted reduction of unnecessary trips, it would be rational to provide an improved
communication facilities that offer low-cost option to trips people would rather avoid and
would rather use ICT to facilitate interactions.
Putting the results all together, this thesis significantly contributed to some key issues in
travel behavior studies. Specifically, the key issues on the capabilities and roles of ICT and
on the inclusion of the concept of social dimensions in the travel behavior analysis. As an
overall conclusion of this study, it is realized that there is a significant interrelationship
among ICT, social dimension and travel behavior.
8.2 Potential applications of the study
This study has various potential applications in terms of its results. For example, the effects
of ICT and social dimension on travel have seen to encompass applications in the following
areas:
1. Environment and health. Based from the result, telecommuting reduces physical
travel it alleviates traffic congestion. When traffic congestion is lessened it
follows that air pollution is reduced making a livable environment. Furthermore,
health-wise and telecommuting being flexible that loosens spatial and temporal
constraints, there will be more that can be time allotted to spend in physical
173
fitness program. Another example is that physicians can possibly monitor their
patients’ physical condition through ICT and the more convenient way is to make
a call through mobile phone.
2. Commerce. ICT may have the potential to reduce the need of movement of
goods at the same time it enables to enhance commercial trade mainly because of
its quick and efficient operations in communication and delivery. In addition,
ICT may also facilitates shopping and purchasing of other goods online without
physically going to the stores or shops. Moreover, ICT may also help provide
essential information of products online or in the internet which the consumers
are able to make some comparisons of a specific product along with the relevant
characteristics.
3. Land use. In the era of good telecommunication, location becomes less imperious
constraint on human activities. To that extent, people can, if they wish, choose to
live and work in some remote locations at a tolerable price and at comfortable
living. For example, people who are telecommuting spend time to do more leisure
activities in the nearby neighborhood as a tradeoff from the daily commute from
home to work. These results to create more recreational provisions and
entertainment sites to accommodate these types of lifestyles for workers. And all
these boils down to the critical creation and careful development of transport and
urban planning.
4. Tourism. By using the capability of internet and other applications of ICT, useful
information on locations and sightseeing spots are found to be disseminated
widely and quickly making it more attractive and convenient for travelers. At the
same time, for those in tourism business, they can easily communicate with their
clients more dynamically. Evidently, according to ITA (2006), there are about
fifty percent of German tourists use internet to get information of their
destinations.
For the moment, these are the potential applications that this study is most likely oriented to.
There may be other applications that this study is of practical applications but as far as this
study is concerned the above mentioned are the most approximate.
174
8.3 Further studies for recommendation
One of the limitations of the study is the consideration of the group of population that could
be augmented into a bigger set of general population in the future endeavors of the study.
Hence, a broader survey for general population is recommended for the future works.
Although the findings of the current study are enriching and useful, there are also new
interesting areas to explore for further study, here are the following:
1. The analysis on the spatial attributes of the respondents to understand more on the
travel behavior of those who do telecommuting. For example, the distance of
those simple tours that those who telecommutes made. This will help verify the
speculations on simple tours made in the nearby coffee corner or adjacent
entertainments shops.
2. Similarly, the inclusion of the spatial attribute in the structure of the social
network characteristics of the respondents. Those members of the social network
that resides in close proximity to the actor might have significant effect on the
frequency of trips especially on social participation.
3. One avenue for future research is to relate social dimension to happiness or
subjective well-being. Social dimension is most likely related to the quality of
life – for example, if a person participates in social activities the more often is
because he might truly feels the sense of belongingness by participating to social
activities and make social interaction, which makes them to repeatedly do it more
just to be contented and happy.
4. The data used for Metro Manila happens to furnish only the out-of-home
activities and that the set of in-home activities, unfortunately, were not able to be
integrated in the survey questionnaire. For this reason, it is hoped to include this
aspect of activities for further research endeavors for it might possess significance
to travel behavior.
5. A consideration of investigation of other countries would be a desirable intention
to examine the effect of ICT use and to know their own perspective of ICT use.
Because even among the developing countries the ICT penetration rate might be
175
totally different.
6. Lastly, this study primarily deals only with mobile phone, landline phone and
internet. Other ICT applications that affect travel might be a good area for further
study, for example, advertisements of travel packages in the internet, information
of most travelled places and less travelled places that can be accessed through
mobile phone internet.
For the time being, the key issues mentioned are left for further study. It is hoped that these
are tackled in the future development of this research.
***
176
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188
APPENDICES
Appendix 1
(Alternative structural models for Chapter 6)
Appendix 2
(Alternative structural models for Chapter 7)
Appendix 3
(Sample of the survey questionnaire of London Area Travel Survey 2001 Household Survey Project Report)
Appendix 4
(Sample of Survey documents in 2007 for university students in Metro Manila, Philippines: Survey cover letter and survey questionnaire)
Appendix 5
(Sample of Survey documents in 2008 for university workers in Metro Manila, Philippines: Survey cover letter and survey questionnaire)
189
Appendix 1 (Alternative structural models for Chapter 6)
190
Alt
erna
tive
str
uctu
ral m
odel
s fo
r C
hapt
er 6
Mod
el C
hisq
uare
= 4
32.2
2 D
f = 1
48 p
< 0
.001
Chi
squa
re
(nul
l mod
el) =
129
0.2
Df =
171
G
oodn
ess-
of-fi
t ind
ex =
0.8
5
Adj
uste
d go
odne
ss-o
f-fit
inde
x =
0.8
0
Mod
el C
hisq
uare
= 4
24.2
Df
= 1
47 p
< 0
.001
C
hisq
uare
(nul
l mod
el) =
129
0.2
Df =
171
G
oodn
ess-
of-fi
t ind
ex =
0.8
4 A
djus
ted
good
ness
-of-f
it in
dex
= 0
.80
Alte
rnat
ive
Mod
el 1
Al
tern
ativ
e M
odel
2
191
Mod
el C
hisq
uare
= 4
25.0
1 D
f = 1
47 p
< 0
.001
C
hisq
uare
(nul
l mod
el) =
129
0.2
Df =
171
G
oodn
ess-
of-fi
t ind
ex =
0.8
4
Adj
uste
d go
odne
ss-o
f-fit
inde
x =
0.8
0
Alte
rnat
ive
Mod
el 3
192
Appendix 2 (Alternative structural models for Chapter 7)
193
Alternative structural models for Chapter 7 Alternative Model 1
Alternative Model 2
194
Alternative Model 3
Alternative Model 4
195
Appendix 3 (Sample of the survey questionnaire of London Area Travel Survey 2001 Household
Survey Project Report)
Au
gust
, 10
MR
S M
embe
r: D
.Wal
sh
L
AT
S I
ND
IVID
UA
L
INT
ER
VIE
W (v
3)
Sam
ple
ID
Nu
mb
er:
Hou
seh
old
No.
(see
Hou
seh
old
Q
ues
tion
nai
re –
A2)
:
1
2
3
Res
pon
den
t’s N
ame
___
____
____
____
____
____
____
____
____
__ P
erso
n N
um
ber
wit
hin
hou
seho
ld*
____
___
(* s
ee B
2 on
Hou
seho
ld Q
uest
ionn
aire
)
Tel
epho
ne N
um
ber
____
____
____
_ _
____
____
____
____
____
____
____
_ In
terv
iew
Dat
e __
____
____
____
____
_
Pro
be if
res
pond
ent h
as m
obile
tele
phon
e an
d c
ircl
e as
app
ropr
iate
: Y
ES
/ N
O
Inte
rvie
wer
Nam
e ___
____
____
____
____
____
____
____
____
____
__ I
.D. #
___
____
____
____
____
____
__
In
terv
iew
Len
gth
(min
s) __
____
___
Si
gned
by
Inte
rvie
wer
. C
heck
ed b
y su
perv
isor
.
YO
U M
US
T C
ON
DU
CT
AN
IN
DIV
IDU
AL
IN
TE
RV
IEW
WIT
H A
LL
HO
US
EH
OL
D
ME
MB
ER
S A
ND
VIS
ITO
RS
AG
ED
5 O
R O
VE
R.
IF I
NT
ER
VIE
WIN
G A
CH
ILD
UN
DE
R T
HE
AG
E O
F 16
, PL
EA
SE
MA
KE
SU
RE
A
PA
RE
NT
/GU
AR
DIA
N S
IGN
S T
HE
FO
LL
OW
ING
CO
NS
EN
T.
PA
RE
NT
AL
CO
NSE
NT
DE
CL
AR
AT
ION
I
here
by g
ive
perm
issi
on t
o R
esea
rch
Inte
rnat
iona
l to
inte
rvie
w m
y ch
ild
as p
art
of t
he L
AT
S 20
01 s
tudy
.
Nam
e of
par
ent/
guar
dian
giv
ing
perm
issi
on:
Sign
atur
e of
par
ent/
guar
dian
:
Dat
e:
T
HE
‘TR
IP S
HE
ET
’ TR
AV
EL
DA
Y A
ND
DA
TE
IS
: D
AY
:___
____
____
____
____
____
____
____
____
__
DA
TE
:___
_ /
___
_ /
200
1 E
NT
ER
TO
TA
L N
UM
BE
R O
F T
RIP
S M
AD
E B
Y I
ND
IVID
UA
L (=
NO
. TR
IP S
HE
ET
S):
E
nsur
e T
rips
are
num
bere
d co
rrec
tly
(i.e
. in
chro
nolo
gica
l ord
er a
cros
s th
e da
y).
TH
E ‘S
EL
F-C
OM
PL
ET
ION
DIA
RY
’* D
AY
AN
D D
AT
E (s
ee ‘S
elf-
Com
plet
ion
Dia
ry D
ay S
elec
tor
Shee
t’) I
S:
DA
Y:_
____
____
____
____
____
____
____
____
____
D
AT
E:_
___
/ _
___
/ 2
001
* A
t th
e en
d of
the
inte
rvie
w, a
nd h
avin
g co
mpl
eted
all
Tri
p Sh
eets
, giv
e re
spon
dent
a S
elf-
Com
plet
ion
Dia
ry, a
nd a
sk
them
to
com
plet
e it
for
all t
rips
the
y m
ake
next
‘XX
X d
ay’.
Mak
e su
re y
ou fu
lly
com
plet
e th
e in
terv
iew
er o
nly
sect
ions
on
the
fron
t pa
ge, a
long
wit
h th
e re
spon
dent
nam
e (n
ext
to ‘D
ear…
.’) a
nd t
he D
ay &
Dat
e fi
elds
.
INT
RO
DU
CT
ION
IF
RE
SP
ON
DE
NT
CO
MP
LE
TE
D H
OU
SE
HO
LD
QU
ES
TIO
NN
AIR
E, S
AY
: I
now
nee
d to
col
lect
som
e sp
ecif
ic in
form
atio
n re
lati
ng to
you
r ow
n tr
avel
hab
its a
nd a
ttit
udes
tow
ards
tran
spor
t in
gene
ral.
FO
R O
TH
ER
HO
US
EH
OL
D M
EM
BE
RS
, SA
Y:
I’v
e co
llec
ted
som
e ge
nera
l inf
orm
atio
n ab
out y
our
hous
ehol
d fr
om…
.(H
ouse
hol
d R
espo
nde
nt)
and
now
nee
d to
col
lect
som
e sp
ecif
ic in
form
atio
n fr
om y
ou r
elat
ing
to tr
avel
ha
bits
and
att
itud
es to
war
ds tr
ansp
ort i
n ge
nera
l.
SE
CT
ION
Dis
1.
DIS
AB
ILIT
IES
Dis
1 D
o yo
u ha
ve a
ny lo
ngst
andi
ng h
ealt
h pr
oble
m o
r di
sabi
lity
that
aff
ects
you
r ab
ility
to tr
avel
or
get a
bout
?
Yes
.....
......
......
......
......
......
......
......
......
......
1
CO
NT
INU
E W
ITH
Dis
2
No
......
......
......
......
......
......
......
......
......
......
2
GO
TO
SE
CT
ION
A
D
is2
SHO
WC
AR
D 1
C
OD
E A
LL
ME
NT
ION
S.
Cou
ld y
ou te
ll m
e w
hich
of
thes
e di
ffic
ulti
es y
our
heal
th p
robl
em o
r di
sabi
lity
cre
ates
for
you
?
Wal
king
......
......
......
......
......
......
......
......
.... 1
H
eari
ng...
......
......
......
......
......
......
......
......
.. 2
See
ing
......
......
......
......
......
......
......
......
......
3
Und
erst
andi
ng ..
......
......
......
......
......
......
.... 4
S
omet
hing
els
e ( W
RIT
E I
N) .
......
......
......
... 5
Dis
3 D
o yo
u ev
er u
se a
whe
elch
air?
Yes
.....
......
......
......
......
......
......
......
......
......
1
No
......
......
......
......
......
......
......
......
......
......
2
D
is4
SHO
WC
AR
D 2
C
OD
E A
LL
ME
NT
ION
S U
ND
ER
CO
LU
MN
Dis
4.
Loo
king
at t
hese
asp
ects
and
type
s of
tran
spor
t, ca
n yo
u te
ll m
e w
hich
, if
any,
you
fin
d im
poss
ible
to c
ope
wit
h w
ith
out h
elp?
Dis
5 C
OD
E A
LL
ME
NT
ION
S U
ND
ER
CO
LU
MN
Dis
5.
And
now
, thi
nkin
g of
the
aspe
cts
and
type
s of
tran
spor
t you
don
’t a
ctua
lly
find
impo
ssib
le, a
re a
ny o
f th
em ju
st d
iffi
cult
for
you
wit
hou
t hel
p?
Dis
4 Im
pos
sib
le
Dis
5 D
iffi
cult
N
one
......
......
......
......
......
......
......
......
.....
.…
…1…
….
.……
1……
. B
uses
that
can
take
whe
elch
airs
.....
.....
.…
…2…
…..
.……
2……
.. B
uses
that
can
’t ta
ke w
heel
chai
rs ..
......
.…
…3…
…..
.……
3……
.. C
oach
es ..
......
......
......
......
......
......
......
....
.……
4……
.. .…
…4…
…..
The
Und
ergr
ound
.....
......
......
......
......
....
.……
5……
.. .…
…5…
…..
Mai
nlin
e tr
ains
.....
......
......
......
......
......
..
.……
6……
.. .…
…6…
…..
Lon
don
taxi
s ...
......
......
......
......
......
......
. .…
…7…
…..
.……
7……
.. C
ars
(as
driv
er) .
......
......
......
......
......
......
.…
…8…
…..
.……
8……
.. C
ars
(as
pass
enge
r) ..
......
......
......
......
....
.……
9……
.. .…
…9…
…..
Wal
king
......
......
......
......
......
......
......
......
.…
…A
……
.. .…
…A
……
..
196
SE
CT
ION
A.
DR
IVIN
G L
ICE
NC
ES
, SE
AS
ON
TIC
KE
TS
& P
AS
SE
S
A1
SHO
WC
AR
D 3
C
OD
E A
LL
ME
NT
ION
S.
IF N
EC
ES
SA
RY
, PR
OB
E L
ICE
NC
E I
S C
UR
RE
NT
AN
D V
AL
ID I
N T
HE
UK
.
Do
you
hold
any
of
thes
e ty
pes
of d
rivi
ng li
cenc
e?
F
ull l
icen
ce -
car
.....
......
......
......
......
......
......
......
.. 1
Ful
l lic
ence
- m
otor
cyc
le o
r m
oped
.....
......
......
.. 2
Pro
visi
onal
lice
nce
- ca
r ....
......
......
......
......
......
.... 3
P
rovi
sion
al li
cenc
e -
mot
or c
ycle
or
mop
ed ..
.....
4 P
SV
lice
nce .
......
......
......
......
......
......
......
......
......
.. 5
HG
V li
cenc
e ...
......
......
......
......
......
......
......
......
.... 6
N
one
of th
ese/
aged
und
er 1
6....
......
......
......
......
... 7
A2
Do
you
curr
ently
hol
d an
y ki
nd o
f pu
blic
tran
spor
t, ta
xi p
ass
or R
ailc
ard,
ent
itli
ng y
ou to
fre
e tr
avel
or
redu
ced
fare
s?
Y
es ..
......
......
......
......
......
......
......
......
......
......
......
.. 1
CO
NT
INU
E W
ITH
A3
No
......
......
......
......
......
......
......
......
......
......
......
.....
2
GO
TO
A4
A
3 SH
OW
CA
RD
4
CO
DE
AL
L M
EN
TIO
NS
.
Whi
ch o
f th
ese
free
pas
ses
or R
ailc
ards
do
you
hold
? F
RE
E P
asse
s:
OA
P/s
enio
r ci
tize
n (F
reed
om P
ass)
issu
ed b
y lo
cal a
utho
rity
.....
... 1
D
isab
led/
blin
d pe
rson
(F
reed
om P
ass)
issu
ed b
y lo
cal a
utho
rity
... 2
T
axic
ard
(iss
ued
by lo
cal a
utho
rity
) ...
......
......
......
......
......
......
......
.. 3
Stu
dent
pas
s (i
ssue
d by
loca
l aut
hori
ty) .
......
......
......
......
......
......
.....
4 S
taff
or
poli
ce p
ass .
......
......
......
......
......
......
......
......
......
......
......
......
. 5
DIS
CO
UN
TE
D P
asse
s/R
ailc
ard
s:
Sen
ior
Rai
lcar
d ...
......
......
......
......
......
......
......
......
......
......
......
......
.... 6
Y
oung
Per
son’
s R
ailc
ard
......
......
......
......
......
......
......
......
......
......
.... 7
N
etw
ork
Rai
lcar
d ...
......
......
......
......
......
......
......
......
......
......
......
......
. 8
Par
tner
’s G
old
Car
d (i
.e. p
artn
er c
an g
et th
em a
dis
coun
t) ..
......
.....
9 F
amil
y R
ailc
ard .
......
......
......
......
......
......
......
......
......
......
......
......
......
A
Oth
er (
WR
ITE
IN
) ....
......
......
......
......
......
......
......
......
......
......
......
......
B
A4
SHO
WC
AR
D 5
CO
DE
AL
L M
EN
TIO
NS
. D
id y
ou h
old
any
of th
ese
pass
es o
r se
ason
tick
ets
for
trav
el in
or
to th
e L
ondo
n ar
ea y
este
rday
(/o
n th
e T
RA
VE
L D
AY
if d
iffe
ren
t).
I am
onl
y in
tere
sted
if it
is v
alid
for
a w
eek
or lo
nge
r, a
nd if
you
or
som
eone
els
e (e
.g. p
artn
er/ r
elat
ive/
empl
oyer
) ha
s ac
tual
ly p
aid
for
it (
i.e. i
t is
not a
fre
e pa
ss).
Bus
pas
s (b
uses
onl
y) ...
......
......
......
......
......
......
......
.. 1
CO
MP
LE
TE
CO
LU
MN
A
Tra
velc
ard
(bus
es, t
ubes
and
trai
ns) .
......
......
......
......
2
CO
MP
LE
TE
CO
LU
MN
B
Sta
tion
to s
tati
on s
easo
n ti
cket
(tu
bes
OR
trai
ns)
.... 3
C
OM
PL
ET
E C
OL
UM
N C
LT
You
th C
ard
(bus
es a
nd tu
bes
only
) ....
......
......
.... 4
C
OM
PL
ET
E C
OL
UM
N D
Non
e of
thes
e ....
......
......
......
......
......
......
......
......
......
.. 5
GO
TO
SE
CT
ION
B
IF P
OS
SIB
LE
, AS
K R
ES
PO
ND
EN
T T
O S
HO
W T
ICK
ET
SO
YO
U C
AN
CH
EC
K.
A –
Bu
s P
ass
det
ails
B –
Tra
velc
ard
d
etai
ls
C
– S
tati
on t
o st
atio
n
seas
on t
ick
et d
etai
ls
D
– L
T Y
outh
Car
d
det
ails
qa2
. Cir
cle
AL
L z
ones
B
us
Pas
s is
val
id f
or:
q
b2.
Cir
cle
AL
L z
ones
T
rave
lcar
d is
val
id f
or:
q
c2. W
rite
in s
tati
ons*
of
valid
ity
q
d2.
Cir
cle
AL
L z
ones
L
T C
ard
is v
alid
for
:
…
1…
…
1…
B
etw
een
:
…1…
…
2…
…
2…
…
2…
…3…
…3…
…3…
…
4…
…
4…
A
nd:
…4…
…
9*…
…5…
…5…
*(
= L
ocal
are
a on
ly)
…
6…
…
6…
…
7*…
*on
e m
ay b
e a
gen
eral
are
a -
* i.
e. s
tati
on o
uts
ide
zon
e 6
-
e.g.
‘L
ondo
n te
rmin
als’
w
rite
in s
tati
on n
ame
her
e:
q
a3. W
hat
per
iod
is it
fo
r?
q
b3.
Wh
at p
erio
d is
it
for?
qc3
. Wh
at p
erio
d is
it
for?
qd
3. W
hat
per
iod
is it
fo
r?
Wee
kly .
......
......
......
.. 1
W
eekl
y ...
......
......
......
1
W
eekl
y ...
......
......
......
1
W
eekl
y ....
......
......
......
1
M
onth
ly ..
......
......
.....
2
Mon
thly
.....
......
......
... 2
Mon
thly
......
......
......
.. 2
M
onth
ly ..
......
......
......
2
3-
mon
ths/
quar
terl
y ...
3
3-m
onth
s/qu
arte
rly
... 3
3-m
onth
s/qu
arte
rly
... 3
3-m
onth
s/qu
arte
rly.
...3
Ann
ual .
......
......
......
.. 4
A
nnua
l ....
......
......
......
4
A
nnua
l ....
......
......
.....
4
Ann
ual .
......
......
......
...4
Oth
er ..
......
......
......
.... 5
Oth
er ...
......
......
......
.... 5
Oth
er ..
......
......
......
.... 5
Oth
er ..
......
......
......
.....5
qa4
. How
mu
ch d
id it
co
st?
q
b4.
How
muc
h d
id it
co
st?
q
c4. H
ow m
uch
did
it
cost
?
qd
4. H
ow m
uch
did
it
cost
?
£___
___
:___
___
p
£_
____
_ :_
____
_ p
£___
___
:___
___
p
£_
____
_ :_
____
_ p
IF
MO
RE
TH
AN
ON
E T
ICK
ET
TY
PE
CIR
CL
ED
AT
A4,
MA
KE
SU
RE
YO
U H
AV
E C
OM
PL
ET
ED
A
CO
LU
MN
FO
R E
AC
H B
EF
OR
E M
OV
ING
ON
.
197
SE
CT
ION
B.
EM
PL
OY
ME
NT
AN
D E
DU
CA
TIO
N
IF R
ES
PO
ND
EN
T I
S A
GE
D U
ND
ER
16,
GO
TO
BB
8. O
TH
ER
WIS
E C
ON
TIN
UE
. B
1 SH
OW
CA
RD
6
O
NE
CO
DE
ON
LY
.
To
whi
ch o
f th
ese
cate
gori
es d
o yo
u be
long
?
IF
RE
SP
ON
DE
NT
SA
YS
MO
RE
TH
AN
ON
E, P
RIO
RIT
ISE
AS
FO
LL
OW
S:
- S
tud
ent
who
wor
ks p
art
tim
e =
stu
den
t -
Stu
den
t w
ho w
ork
s fu
ll t
ime
= f
ull
tim
e w
ork
er
- If
look
ing
afte
r h
ome/
reti
red
bu
t w
ork
par
t ti
me
= p
art
tim
e w
ork
er
IF R
ES
PO
ND
EN
T I
S ‘
TE
MP
ING
’, P
RO
BE
HO
W M
AN
Y H
OU
RS
TH
EY
AR
E W
OR
KIN
G T
HIS
WE
EK
, AN
D
CO
DE
AS
FU
LL
/PA
RT
TIM
E E
MP
LO
YE
D A
S A
PP
RO
PR
IAT
E.
Ful
l-ti
me
paid
em
ploy
men
t (30
+ h
ours
a w
eek)
.....
......
......
......
......
......
.. 1
P
art-
tim
e pa
id e
mpl
oym
ent (
less
than
30
hour
s a
wee
k) ..
......
......
......
.... 2
G
O T
O S
EC
TIO
N B
A
Ful
l-ti
me
self
-em
ploy
men
t (30
+ h
ours
a w
eek)
......
......
......
......
......
......
.. 3
(W
OR
KE
RS
)
Par
t-ti
me
self
-em
ploy
men
t (le
ss th
an 3
0 ho
urs
a w
eek)
.....
......
......
......
.. 4
Stu
dent
/sch
ool p
upil
......
......
......
......
......
......
......
......
......
......
......
......
......
. 5
GO
TO
SE
CT
ION
BB
(S
TU
DE
NT
S)
Wai
ting
to ta
ke u
p a
job
......
......
......
......
......
......
......
......
......
......
......
......
.. 6
U
nem
ploy
ed a
nd lo
okin
g fo
r w
ork
.....
......
......
......
......
......
......
......
......
... 7
U
nabl
e to
wor
k be
caus
e of
long
-ter
m il
lnes
s or
dis
abil
ity .
......
......
......
... 8
GO
TO
SE
CT
ION
BC
Ret
ired
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
..... 9
(NO
N W
OR
KIN
G)
Loo
king
aft
er h
ome
or f
amil
y ...
......
......
......
......
......
......
......
......
......
......
.. 10
Oth
er (
WR
ITE
IN
) ....
......
......
......
......
......
......
......
......
......
......
......
......
......
.. 11
SE
CT
ION
BA
- F
UL
L &
PA
RT
TIM
E W
OR
KE
RS
(‘E
MP
LO
YE
D’
& S
EL
F-E
MP
LO
YE
D)
BA
1 IF
RE
SP
ON
DE
NT
HA
S M
OR
E T
HA
N O
NE
JO
B, A
SK
AB
OU
T T
HE
IR M
AIN
JO
B.
IF R
ET
IRE
D B
UT
WO
RK
ING
DU
RIN
G R
ET
IRE
ME
NT
, AS
K A
BO
UT
PR
EV
IOU
S O
CC
UP
AT
ION
.
I no
w n
eed
to c
olle
ct s
ome
info
rmat
ion
abou
t you
r jo
b.
Fir
st o
f al
l, w
hat i
s yo
ur f
ull j
ob ti
tle?
(W
RIT
E I
N)
P
RO
BE
FO
R J
OB
QU
AL
IFIC
AT
ION
S H
EL
D/G
RA
DE
IF
CIV
IL S
ER
VA
NT
/NU
RS
E E
TC
.
B
A2
Wha
t are
the
mai
n th
ings
you
do
in y
our
job?
(W
RIT
E I
N)
P
RO
BE
: -
IND
US
TR
Y/T
YP
E O
F E
ST
AB
LIS
HM
EN
T
- W
HE
TH
ER
JO
B I
S C
LE
RIC
AL
OR
MA
NU
AL
(I
F I
MP
LIC
IT I
N B
A1,
WR
ITE
SA
ME
AS
BA
1)
BA
3 SH
OW
CA
RD
7
D
o yo
u ha
ve a
n oc
cupa
tion
whe
re d
rivi
ng o
r tr
avel
ling
aro
und
is a
n in
tegr
al p
art o
f th
e jo
b, li
ke o
ne o
f th
ese?
(DO
NO
T I
NC
LU
DE
OF
FIC
E W
OR
KE
RS
WH
O M
AY
TR
AV
EL
TO
SE
E C
LIE
NT
S).
Pub
lic
tran
spor
t veh
icle
dri
ver .
......
......
......
......
......
......
......
1
Tax
i/m
ini c
ab d
rive
r ....
......
......
......
......
......
......
......
......
......
. 2
Goo
ds v
ehic
le d
rive
r ....
......
......
......
......
......
......
......
......
......
3
Dri
ve a
n em
erge
ncy
or p
atro
l veh
icle
.....
......
......
......
......
... 4
C
ar, m
otor
- or
ped
al-c
ycle
cou
rier
.....
......
......
......
......
......
.. 5
Doo
r-to
-doo
r se
llin
g ...
......
......
......
......
......
......
......
......
......
. 6
Hom
e de
live
ry (
post
, mil
k et
c) ..
......
......
......
......
......
......
.... 7
H
ome
serv
ice
wor
ker
(plu
mbe
r, e
lect
rici
an e
tc.)
.....
......
.... 8
O
ther
occ
upat
ion
whe
re d
rivi
ng/t
rave
llin
g ar
ound
is
an in
tegr
al p
art o
f th
e jo
b ( W
RIT
E I
N) .
......
......
......
......
......
. 9
R
espo
nde
nt d
oes
not
wor
k as
an
y of
the
abov
e ....
......
......
A
CO
MP
LE
TE
TH
IS C
OL
UM
N I
F R
ES
PO
ND
EN
T I
S
AN
‘E
MP
LO
YE
E’
(CO
DE
1 o
r 2
AT
B1)
CO
MP
LE
TE
TH
IS C
OL
UM
N I
F R
ES
PO
ND
EN
T I
S
SE
LF
-EM
PL
OY
ED
(C
OD
E 3
or
4 A
T B
1)
BA
4 A
re y
ou a
man
ager
?
BA
4 D
o yo
u ha
ve a
ny e
mpl
oyee
s?
Yes
.....
......
.....
1
GO
TO
BA
6
Y
es ..
......
......
.. 1
GO
TO
BA
6
No
......
......
.....
2
CO
NT
INU
E W
ITH
BA
5
No.
......
......
.....
2
CO
NT
INU
E W
ITH
BA
5
BA
5 D
o yo
u su
perv
ise
othe
r st
aff?
BA
5 D
o yo
u su
perv
ise
othe
r st
aff
that
are
not
you
r em
ploy
ees?
Y
es ..
......
......
.. 1
Y
es ..
......
......
.. 1
No
......
......
.....
2
GO
TO
BA
7
No.
......
......
.....
2
GO
TO
BA
7
BA
6 H
ow m
any
peop
le d
o yo
u m
anag
e/su
perv
ise?
B
A6
How
man
y pe
ople
do
you
empl
oy/m
anag
e/
supe
rvis
e in
tota
l?
1-24
.....
......
.... 1
1-24
.....
......
.... 1
25
or
mor
e ....
. 2
25
or
mor
e ....
. 2
BA
7 H
ow m
any
peop
le a
re e
mpl
oyed
at t
he s
ite
whe
re y
ou w
ork?
BA
7 H
ow m
any
peop
le a
re e
mpl
oyed
at t
he s
ite
whe
re y
ou w
ork?
1-
24 ..
......
......
. 1
1-
24 ..
......
......
. 1
25 o
r m
ore .
.... 2
25 o
r m
ore .
.... 2
C
ON
TIN
UE
WIT
H B
A8
C
ON
TIN
UE
WIT
H B
A8
198
BA
8 SH
OW
CA
RD
8
ON
E C
OD
E O
NL
Y.
IF T
EM
P/C
ON
TR
AC
TO
R A
ND
TR
AV
EL
LIN
G T
O C
UR
RE
NT
PL
AC
E O
F W
OR
K F
OR
A
MO
NT
H O
R M
OR
E, C
OD
E A
S 1
.
Ple
ase
tell
me
whi
ch o
f th
ese
stat
emen
ts b
est d
escr
ibes
you
r us
ual t
rave
l to
wor
k?
I tr
avel
to th
e sa
me
plac
e of
wor
k ne
arly
eve
ry d
ay ..
......
......
......
......
.. 1
C
ON
TIN
UE
WIT
H B
A9
I us
uall
y tr
avel
fro
m h
ome
to d
iffe
rent
wor
k pl
aces
.....
......
......
......
.....
2
I
usua
lly w
ork
at h
ome .
......
......
......
......
......
......
......
......
......
......
......
......
3
GO
TO
BA
15
B
A9
RE
AD
OU
T E
AC
H S
TA
TE
ME
NT
AN
D C
OD
E T
RU
E O
R F
AL
SE
FO
R E
AC
H.
Thi
nkin
g of
you
r us
ual c
urre
nt w
ork
patte
rn, a
re th
e fo
llow
ing
stat
emen
ts tr
ue o
r fa
lse?
TR
UE
F
AL
SE
a. I
get
to w
ork
at a
bout
the
sam
e ti
me
ever
y da
y ....
......
......
......
......
......
......
......
......
1 ...
......
......
.. 2
b. I
gen
eral
ly g
et to
wor
k be
twee
n 8a
m a
nd 9
.30a
m ..
......
......
......
......
......
......
......
... 1
......
......
.....
2
c. I
am
abl
e to
wor
k ‘f
lexi
time’
if I
wis
h ...
......
......
......
......
......
......
......
......
......
......
.. 1 ..
......
......
... 2
d. I
reg
ular
ly w
ork
diff
eren
t shi
fts .
......
......
......
......
......
......
......
......
......
......
......
......
.. 1 ..
......
......
... 2
BA
10
Wha
t is
the
addr
ess
of y
our
usua
l pla
ce o
f w
ork?
W
RIT
E I
N F
UL
L A
DD
RE
SS
DE
TA
ILS
.
P
leas
e ca
n I
star
t by
aski
ng:-
CO
MP
AN
Y/S
HO
P/P
LA
CE
NA
ME
NU
MB
ER
& S
TR
EE
T N
AM
E
TO
WN
/LO
ND
ON
AR
EA
PO
ST
CO
DE
*
(* I
F N
OT
KN
OW
N, P
RO
VID
E F
UR
TH
ER
DE
TA
IL O
F L
OC
AT
ION
)
BA
11
How
long
ago
did
you
sta
rt w
orki
ng a
t tha
t loc
atio
n?
YE
AR
S
MO
NT
HS
BA
12
PR
OB
E A
ND
CO
DE
AL
L M
ET
HO
DS
US
ED
IN
CO
LU
MN
BA
12a
AN
D M
AIN
ME
TH
OD
US
ED
(i.e
. LO
NG
ES
T
DIS
TA
NC
E)
IN C
OL
UM
N B
A12
b.
Thi
nkin
g of
you
r u
sual
* m
eans
of
trav
el to
wor
k……
(a)
whi
ch m
etho
ds o
f tr
ansp
ort d
o yo
u us
e?
( * i.
e. H
OW
TH
EY
TR
AV
EL
MO
ST
DA
YS
)
……
(b)
whi
ch c
over
s th
e lo
nges
t dis
tanc
e?
B
A12
a-A
ll m
etho
ds
BA
12b
-Mai
n m
eth
od
Car
(dr
iver
) ...
......
......
......
......
......
......
......
.
.……
1……
.. .…
…1*
*……
..
Sm
all v
an/m
inib
us (
driv
er) .
......
......
......
..
. .…
…2.
……
. .…
…2*
*……
.. IF
AN
Y S
HA
DE
D (
**)
Mot
or c
ycle
(ri
der)
.....
......
......
......
......
.....
.
.……
3……
.. .…
…3*
*……
.. C
OD
ES
CIR
CL
ED
Ped
al b
ike .
......
......
......
......
......
......
......
.....
.
.……
4……
.. .…
…4*
*……
..
GO
TO
BA
13
Car
(pa
ssen
ger)
.....
......
......
......
......
......
....
. .…
…5…
…..
.……
5……
..
Sm
all v
an/m
inib
us (
pass
enge
r) ..
......
......
. .
.……
6……
.. .…
…6…
…..
M
otor
cyc
le (
pill
ion)
.....
......
......
......
......
..
. .…
…7…
…..
.……
7……
..
Bus
.....
......
......
......
......
......
......
......
......
.....
.
.……
8……
.. .…
…8…
…..
T
ube
......
......
......
......
......
......
......
......
......
..
. .…
…9…
…..
.……
9……
.. O
TH
ER
WIS
E,
Tra
in ..
......
......
......
......
......
......
......
......
......
.
.……
A…
….
.……
A…
….
G
O T
O B
A15
DL
R ..
......
......
......
......
......
......
......
......
......
.
.……
B…
….
.……
B…
….
T
ram
.....
......
......
......
......
......
......
......
......
...
. .…
…C
……
. .…
…C
……
.
Wal
k ...
......
......
......
......
......
......
......
......
.....
.
.……
D…
….
.……
D…
….
O
ther
(W
RIT
E I
N) .
......
......
......
......
......
.....
.
.……
E…
….
.……
E…
….
BA
13
RE
AD
OU
T A
S A
PP
RO
PR
IAT
E.
O
NE
CO
DE
ON
LY
.
Do
you
actu
ally
…
par
k yo
ur c
ar/v
an/m
otor
cyc
le a
t or
near
you
r w
ork
loca
tion
?
…
leav
e yo
ur p
edal
bik
e at
or
near
you
r w
ork
loca
tion
?
Yes
, par
k ca
r/va
n/m
otor
cyc
le ..
......
......
... 1
C
ON
TIN
UE
WIT
H B
A14
a
Yes
, lea
ve p
edal
bik
e ...
......
......
......
......
.... 2
G
O T
O B
A14
b
No
......
......
......
......
......
......
......
......
......
......
3
GO
TO
BA
15
BA
14a
ON
E C
OD
E O
NL
Y.
Whe
re d
o yo
u no
rmal
ly p
ark
your
car
/van
/mot
or c
ycle
? C
ar p
ark/
allo
cate
d sp
ace
at s
ite .
......
......
......
......
......
......
.....
1 O
ther
par
king
arr
ange
men
ts p
rovi
ded
with
job
......
......
.....
2 P
ubli
c ca
r pa
rk (
e.g.
Pay
&D
ispl
ay/N
CP
) –
paid
* ...
......
.... 3
P
ubli
c ca
r pa
rk (
e.g.
Pay
&D
ispl
ay/N
CP
) -
free
.....
......
......
4
On
stre
et –
pai
d* ..
......
......
......
......
......
......
......
......
......
......
.. 5
On
stre
et -
fre
e ...
......
......
......
......
......
......
......
......
......
......
.... 6
O
ther
(W
RIT
E I
N A
ND
PR
OB
E I
F P
AID
* O
R F
RE
E) .
......
......
7
*PR
OB
E W
HO
PA
ID.
IF N
OT
PA
ID F
OR
BY
RE
SP
ON
DE
NT
/HO
US
EH
OL
D M
EM
BE
R (
E.G
. EM
PL
OY
ER
PA
ID),
T
HE
N C
OD
E A
BO
VE
AS
‘F
RE
E’
RA
TH
ER
TH
AN
‘P
AID
’.
N
OW
GO
TO
BA
15
BA
14b
ON
E C
OD
E O
NL
Y.
Whe
re d
o yo
u no
rmal
ly le
ave
your
ped
al b
ike?
C
ycle
rac
k/sh
ed a
t sit
e ...
......
......
......
......
......
......
......
......
.... 1
O
ther
des
igna
ted
area
for
bic
ycle
s at
sit
e ...
......
......
......
.....
2 O
n pa
vem
ent/
stre
et ..
......
......
......
......
......
......
......
......
......
.... 3
O
ther
(W
RIT
E I
N) .
......
......
......
......
......
......
......
......
......
......
.. 4
N
OW
GO
TO
BA
15
199
BA
15
SHO
WC
AR
D 9
CO
DE
AL
L M
EN
TIO
NS
UN
DE
R C
OL
UM
N B
A15
.
Thi
s li
st s
how
s va
riou
s fa
cili
ties
and
ben
efit
s th
at e
mpl
oyer
s pr
ovid
e w
ith
resp
ect t
o tr
avel
. W
hich
, if
any,
do
you
use
/ben
efit
fro
m?
BA
16
CO
DE
AL
L M
EN
TIO
NS
UN
DE
R C
OL
UM
N B
A16
.
And
whi
ch o
ther
s, if
any
, are
ava
ilab
le to
you
?
B
A15
–
Use
/ben
efit
fr
om
BA
16 –
A
vail
able
to
you
V
ehic
le p
rovi
ded
for
busi
ness
use
onl
y ...
......
......
......
......
......
......
......
......
. …
..1…
.. …
..1…
..
Veh
icle
pro
vide
d fo
r bo
th p
riva
te a
nd b
usin
ess
use
......
......
......
......
......
...
…..2
…..
…..2
…..
Cas
h in
lieu
of
vehi
cle
......
......
......
......
......
......
......
......
......
......
......
......
......
…..3
…..
…..3
…..
Con
trib
utio
n to
cos
t of
purc
hase
of
priv
ate
car .
......
......
......
......
......
......
....
…..4
…..
…..4
…..
Pre
fere
ntia
l/in
tere
st-f
ree
loan
s fo
r ca
r pu
rcha
se ..
......
......
......
......
......
......
. …
..5…
.. …
..5…
..
Fue
l for
bus
ines
s us
e on
ly ...
......
......
......
......
......
......
......
......
......
......
......
....
…..6
…..
…..6
…..
Fue
l for
bot
h bu
sine
ss a
nd p
riva
te u
se ..
......
......
......
......
......
......
......
......
....
…..7
…..
…..7
…..
Mil
eage
all
owan
ce f
or o
wn
car
if u
sed
for
empl
oyer
s bu
sine
ss ..
......
......
. …
..8…
.. …
..8…
..
Con
trib
utio
n to
run
ning
cos
ts (
e.g.
mai
nten
ance
/ins
uran
ce)
of p
riva
te c
ar
…..9
…..
…..9
…..
Fre
e or
sub
sidi
sed
park
ing
spac
es ...
......
......
......
......
......
......
......
......
......
....
…..A
…..
…..A
…..
Sto
rage
fac
ilit
ies
for
peda
l bik
es ...
......
......
......
......
......
......
......
......
......
......
…
..B…
.. …
..B…
..
Mil
eage
all
owan
ce f
or p
edal
bik
e tr
avel
.....
......
......
......
......
......
......
......
....
…..C
…..
…..C
…..
Sho
wer
s an
d ch
angi
ng f
acil
itie
s ...
......
......
......
......
......
......
......
......
......
......
…
..D…
.. …
..D…
..
Fre
e or
sub
sidi
sed
publ
ic tr
ansp
ort s
easo
n ti
cket
......
......
......
......
......
......
..
…..E
…..
…..E
…..
Sea
son
tick
et lo
an ..
......
......
......
......
......
......
......
......
......
......
......
......
......
.....
…
..F…
.. …
..F…
..
Non
e ....
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
…
..G…
.. …
..G…
..
BA
17 D
o yo
u ha
ve a
cces
s to
a la
p to
p or
com
pute
r at
hom
e?
Yes
.....
......
......
......
......
......
......
......
......
......
1
CO
NT
INU
E W
ITH
BA
18
No
......
......
......
......
......
......
......
......
......
......
2
GO
TO
SE
CT
ION
C
B
A18
D
oes
the
com
pute
r/la
p to
p ha
ve a
n em
ail o
r in
tern
et c
onne
ctio
n?
Yes
.....
......
......
......
......
......
......
......
......
......
1
No
......
......
......
......
......
......
......
......
......
......
2
BA
19
Do
you
use
the
com
pute
r/la
p to
p to
wor
k fr
om h
ome?
Yes
.....
......
......
......
......
......
......
......
......
......
1
CO
NT
INU
E W
ITH
BA
20
No
......
......
......
......
......
......
......
......
......
......
2
GO
TO
SE
CT
ION
C
BA
20
How
man
y ho
urs
a w
eek,
on
aver
age,
do
you
use
the
com
pute
r/la
p to
p to
wor
k fr
om h
ome?
WR
ITE
IN
TO
TA
L T
O T
HE
NE
AR
ES
T H
OU
R:
NO
W G
O T
O S
EC
TIO
N C
SE
CT
ION
BB
- S
TU
DE
NT
S
B
B1a
A
part
fro
m c
asua
l or
holi
day
wor
k, h
ave
you
been
in f
ull-
or
part
-tim
e pa
id w
ork
duri
ng th
e la
st 1
0 ye
ars?
Yes
.....
......
......
......
......
......
......
......
......
......
1
CO
NT
INU
E W
ITH
BB
1b
No
......
......
......
......
......
......
......
......
......
......
2
GO
TO
BB
8
B
B1b
O
NE
CO
DE
ON
LY
.
In y
our
mos
t rec
ent j
ob, w
ere
you
an e
mpl
oyee
or
wer
e yo
u se
lf-e
mpl
oyed
? E
mpl
oyee
.....
......
......
......
......
......
......
......
.. 1
Sel
f-em
ploy
ed ..
......
......
......
......
......
......
.... 2
BB
2 IF
RE
SP
ON
DE
NT
HA
D M
OR
E T
HA
N O
NE
JO
B, A
SK
AB
OU
T T
HE
IR M
AIN
JO
B.
I no
w n
eed
to c
olle
ct s
ome
info
rmat
ion
abou
t you
r m
ost r
ecen
t job
.
Fir
st o
f al
l, w
hat w
as th
e fu
ll ti
tle
of y
our
mos
t rec
ent j
ob?
(W
RIT
E I
N)
P
RO
BE
FO
R J
OB
QU
AL
IFIC
AT
ION
S H
EL
D/G
RA
DE
IF
CIV
IL S
ER
VA
NT
/NU
RS
E E
TC
.
B
B3
Wha
t wer
e th
e m
ain
thin
gs y
ou d
id in
that
job?
(W
RIT
E I
N)
P
RO
BE
: -
IND
US
TR
Y/T
YP
E O
F E
ST
AB
LIS
HM
EN
T
- W
HE
TH
ER
JO
B W
AS
CL
ER
ICA
L O
R M
AN
UA
L
(IF
IM
PL
ICIT
IN
BB
2, W
RIT
E S
AM
E A
S B
B2)
C
OM
PL
ET
E T
HIS
CO
LU
MN
IF
RE
SP
ON
DE
NT
WA
S
AN
‘E
MP
LO
YE
E’
(CO
DE
1 A
T B
B1b
AB
OV
E)
C
OM
PL
ET
E T
HIS
CO
LU
MN
IF
RE
SP
ON
DE
NT
W
AS
SE
LF
-EM
PL
OY
ED
(C
OD
E 2
AT
BB
1b A
BO
VE
)
BB
4 W
ere
you
a m
anag
er?
B
B4
Did
you
hav
e an
y em
ploy
ees?
Y
es ..
......
......
.. 1
GO
TO
BB
6
Y
es ..
......
......
.. 1
GO
TO
BB
6
No
......
......
.....
2
CO
NT
INU
E W
ITH
BB
5
No.
......
......
.....
2
CO
NT
INU
E W
ITH
BB
5
BB
5 D
id y
ou s
uper
vise
oth
er s
taff
?
BB
5 D
id y
ou s
uper
vise
oth
er s
taff
that
wer
e no
t yo
ur e
mpl
oyee
s?
Yes
.....
......
.....
1
Yes
.....
......
.....
1 N
o ...
......
......
.. 2
GO
TO
BB
7
No.
......
......
.....
2
GO
TO
BB
7
BB
6 H
ow m
any
peop
le d
id y
ou m
anag
e/su
perv
ise?
B
B6
How
man
y pe
ople
did
you
em
ploy
/man
age/
su
perv
ise
in to
tal?
1-24
.....
......
.... 1
1-24
.....
......
.... 1
25
or
mor
e ....
. 2
25
or
mor
e ....
. 2
BB
7 H
ow m
any
peop
le w
ere
empl
oyed
at t
he s
ite
whe
re y
ou w
orke
d?
B
B7
How
man
y pe
ople
wer
e em
ploy
ed a
t the
sit
e w
here
you
wor
ked?
1-
24 ..
......
......
. 1
1-
24 ..
......
......
. 1
25 o
r m
ore .
.... 2
25 o
r m
ore .
.... 2
C
ON
TIN
UE
WIT
H B
B8
C
ON
TIN
UE
WIT
H B
B8
200
BB
8 Is
this
(i.e
. sam
ple
addr
ess)
you
r us
ual t
erm
-tim
e ho
me
addr
ess?
Yes
.....
......
......
......
......
......
. 1
CO
NT
INU
E W
ITH
BB
9
No
......
......
......
......
......
......
. 2
GO
TO
SE
CT
ION
C
B
B9
WR
ITE
IN
FU
LL
AD
DR
ES
S D
ET
AIL
S.
IF M
OR
E T
HA
N O
NE
SIT
E, P
RO
BE
FO
R M
AIN
/MO
ST
OF
TE
N V
ISIT
ED
.
Wha
t is
the
addr
ess
of y
our
scho
ol/c
olle
ge/u
nive
rsity
?
P
leas
e ca
n I
star
t by
aski
ng:-
SC
HO
OL
/CO
LL
EG
E/U
NIV
ER
SIT
Y N
AM
E
NU
MB
ER
& S
TR
EE
T N
AM
E
TO
WN
/LO
ND
ON
AR
EA
PO
ST
CO
DE
*
(* I
F N
OT
KN
OW
N, P
RO
VID
E F
UR
TH
ER
DE
TA
IL O
F L
OC
AT
ION
)
BB
10
Dur
ing
term
tim
e, h
ow o
ften
do
you
trav
el th
ere?
5 or
mor
e da
ys a
wee
k ...
... 1
3-
4 da
ys a
wee
k ....
......
......
. 2
1-2
days
a w
eek .
......
......
.... 3
L
ess
than
onc
e a
wee
k ...
... 4
BB
11
How
long
ago
did
you
sta
rt g
oing
to th
at s
choo
l/co
lleg
e/un
iver
sity
?
YE
AR
S
MO
NT
HS
BB
12
PR
OB
E A
ND
CO
DE
AL
L M
ET
HO
DS
US
ED
IN
CO
LU
MN
BB
12a
AN
D M
AIN
ME
TH
OD
US
ED
(i.e
. LO
NG
ES
T
DIS
TA
NC
E)
IN C
OL
UM
N B
B12
b.
Thi
nkin
g of
you
r u
sual
* m
eans
of
trav
el to
sch
ool/
coll
ege/
univ
ersi
ty…
……
. ( *
i.e.
HO
W T
HE
Y T
RA
VE
L M
OS
T D
AY
S)
……
(a)
whi
ch m
etho
ds o
f tr
ansp
ort d
o yo
u us
e
…
…(b
) w
hich
cov
ers
the
long
est d
ista
nce?
BB
12a-
All
met
hod
s B
B12
b-M
ain
met
hod
Car
(dr
iver
) ...
......
......
......
......
......
......
......
.
.……
1……
.. .…
…1*
*……
..
Sm
all v
an/m
inib
us (
driv
er) .
......
......
......
..
. .…
…2.
……
. .…
…2*
*……
.. IF
AN
Y S
HA
DE
D (
**)
Mot
or c
ycle
(ri
der)
.....
......
......
......
......
.....
.
.……
3……
.. .…
…3*
*……
.. C
OD
ES
CIR
CL
ED
Ped
al b
ike .
......
......
......
......
......
......
......
.....
.
.……
4……
.. .…
…4*
*……
..
GO
TO
BB
13
Car
(pa
ssen
ger)
.....
......
......
......
......
......
....
. .…
…5…
…..
.……
5……
..
Sm
all v
an/m
inib
us (
pass
enge
r) ..
......
......
. .
.……
6……
.. .…
…6…
…..
M
otor
cyc
le (
pill
ion)
.....
......
......
......
......
..
. .…
…7…
…..
.……
7……
..
Bus
.....
......
......
......
......
......
......
......
......
.....
.
.……
8……
.. .…
…8…
…..
T
ube
......
......
......
......
......
......
......
......
......
..
. .…
…9…
…..
.……
9……
.. O
TH
ER
WIS
E,
Tra
in ..
......
......
......
......
......
......
......
......
......
.
.……
A…
….
.……
A…
….
G
O T
O S
EC
TIO
N C
DL
R ..
......
......
......
......
......
......
......
......
......
.
.……
B…
….
.……
B…
….
T
ram
.....
......
......
......
......
......
......
......
......
...
. .…
…C
……
. .…
…C
……
.
Wal
k ...
......
......
......
......
......
......
......
......
.....
.
.……
D…
….
.……
D…
….
O
ther
(W
RIT
E I
N) .
......
......
......
......
......
.....
.
.……
E…
….
.……
E…
….
BB
13
RE
AD
OU
T A
S A
PP
RO
PR
IAT
E.
O
NE
CO
DE
ON
LY
.
Do
you
actu
ally
…
par
k yo
ur c
ar/v
an/m
otor
cyc
le a
t or
near
you
r w
ork
loca
tion
?
…
leav
e yo
ur p
edal
bik
e at
or
near
you
r w
ork
loca
tion
?
Yes
, par
k ca
r/va
n/m
otor
cyc
le ..
......
......
... 1
C
ON
TIN
UE
WIT
H B
B14
a
Yes
, lea
ve p
edal
bik
e ...
......
......
......
......
.... 2
G
O T
O B
B14
b
No
......
......
......
......
......
......
......
......
......
......
3
GO
TO
SE
CT
ION
C
BB
14a
ON
E C
OD
E O
NL
Y.
Whe
re d
o yo
u no
rmal
ly p
ark
your
car
/van
/mot
or c
ycle
? C
ar p
ark/
allo
cate
d sp
ace
at s
ite .
......
......
......
......
......
......
.....
1 O
ther
par
king
arr
ange
men
ts p
rovi
ded
with
job
......
......
.....
2 P
ubli
c ca
r pa
rk (
e.g.
Pay
&D
ispl
ay/N
CP
) –
paid
* ...
......
.... 3
P
ubli
c ca
r pa
rk (
e.g.
Pay
&D
ispl
ay/N
CP
) -
free
.....
......
......
4
On
stre
et –
pai
d* ..
......
......
......
......
......
......
......
......
......
......
.. 5
On
stre
et -
fre
e ...
......
......
......
......
......
......
......
......
......
......
.... 6
O
ther
(W
RIT
E I
N A
ND
PR
OB
E I
F P
AID
* O
R F
RE
E) .
......
......
7
*PR
OB
E W
HO
PA
ID.
IF N
OT
PA
ID F
OR
BY
RE
SP
ON
DE
NT
/HO
US
EH
OL
D M
EM
BE
R (
E.G
. EM
PL
OY
ER
PA
ID),
T
HE
N C
OD
E A
BO
VE
AS
‘F
RE
E’
RA
TH
ER
TH
AN
‘P
AID
’.
N
OW
GO
TO
SE
CT
ION
C
BB
14b
ON
E C
OD
E O
NL
Y.
Whe
re d
o yo
u no
rmal
ly le
ave
your
ped
al b
ike?
C
ycle
rac
k/sh
ed a
t sit
e ...
......
......
......
......
......
......
......
......
.... 1
O
ther
des
igna
ted
area
for
bic
ycle
s at
sit
e ...
......
......
......
.....
2 O
n pa
vem
ent/
stre
et ..
......
......
......
......
......
......
......
......
......
.... 3
O
ther
(W
RIT
E I
N) .
......
......
......
......
......
......
......
......
......
......
.. 4
N
OW
GO
TO
SE
CT
ION
C
201
SE
CT
ION
BC
– N
ON
-WO
RK
ING
BC
1a
Apa
rt f
rom
cas
ual o
r ho
lida
y w
ork,
hav
e yo
u be
en in
ful
l- o
r pa
rt-t
ime
paid
wor
k du
ring
the
last
10
year
s?
Yes
.....
......
......
......
......
......
......
......
......
......
1
CO
NT
INU
E W
ITH
BC
1b
No
......
......
......
......
......
......
......
......
......
......
2
GO
TO
BC
8
B
C1b
O
NE
CO
DE
ON
LY
.
In y
our
mos
t rec
ent j
ob, w
ere
you
an e
mpl
oyee
or
wer
e yo
u se
lf-e
mpl
oyed
?
Em
ploy
ee ..
......
......
......
......
......
......
......
.....
1 S
elf-
empl
oyed
.....
......
......
......
......
......
......
. 2
B
C2
IF R
ES
PO
ND
EN
T H
AD
MO
RE
TH
AN
ON
E J
OB
, AS
K A
BO
UT
TH
EIR
MA
IN J
OB
.
I no
w n
eed
to c
olle
ct s
ome
info
rmat
ion
abou
t you
r m
ost r
ecen
t job
.
Fir
st o
f al
l, w
hat w
as th
e fu
ll ti
tle
of y
our
mos
t rec
ent j
ob?
(W
RIT
E I
N)
PR
OB
E F
OR
JO
B Q
UA
LIF
ICA
TIO
NS
HE
LD
/GR
AD
E I
F C
IVIL
SE
RV
AN
T/N
UR
SE
ET
C.
B
C3
Wha
t wer
e th
e m
ain
thin
gs y
ou d
id in
that
job?
(W
RIT
E I
N)
P
RO
BE
: -
IND
US
TR
Y/T
YP
E O
F E
ST
AB
LIS
HM
EN
T
- W
HE
TH
ER
JO
B W
AS
CL
ER
ICA
L O
R M
AN
UA
L
(IF
IM
PL
ICIT
IN
BC
2, W
RIT
E S
AM
E A
S B
C2)
C
OM
PL
ET
E T
HIS
CO
LU
MN
IF
RE
SP
ON
DE
NT
WA
S
AN
‘E
MP
LO
YE
E’
(CO
DE
1 A
T B
C1b
AB
OV
E)
C
OM
PL
ET
E T
HIS
CO
LU
MN
IF
RE
SP
ON
DE
NT
W
AS
SE
LF
-EM
PL
OY
ED
(C
OD
E 2
AT
BC
1b A
BO
VE
)
BC
4 W
ere
you
a m
anag
er?
B
C4
Did
you
hav
e an
y em
ploy
ees?
Y
es ..
......
......
.. 1
GO
TO
BC
6
Y
es ..
......
......
.. 1
GO
TO
BC
6
No
......
......
.....
2
CO
NT
INU
E W
ITH
BC
5
No.
......
......
.....
2
CO
NT
INU
E W
ITH
BC
5
BC
5 D
id y
ou s
uper
vise
oth
er s
taff
?
BC
5 D
id y
ou s
uper
vise
oth
er s
taff
that
wer
e no
t yo
ur e
mpl
oyee
s?
Yes
.....
......
.....
1
Yes
.....
......
.....
1 N
o ...
......
......
.. 2
GO
TO
BC
7
No.
......
......
.....
2
GO
TO
BC
7
BC
6 H
ow m
any
peop
le d
id y
ou m
anag
e/su
perv
ise?
B
C6
How
man
y pe
ople
did
you
em
ploy
/man
age/
su
perv
ise
in to
tal?
1-24
.....
......
.... 1
1-24
.....
......
.... 1
25
or
mor
e ....
. 2
25
or
mor
e ....
. 2
BC
7 H
ow m
any
peop
le w
ere
empl
oyed
at t
he s
ite
whe
re y
ou w
orke
d?
B
C7
How
man
y pe
ople
wer
e em
ploy
ed a
t the
sit
e w
here
you
wor
ked?
1-
24 ..
......
......
. 1
1-
24 ..
......
......
. 1
25 o
r m
ore .
.... 2
25 o
r m
ore .
.... 2
C
ON
TIN
UE
WIT
H B
C8
CO
NT
INU
E W
ITH
BC
8 B
C8
Do
you
have
acc
ess
to a
lap
top
or h
ome
com
pute
r at
hom
e?
Yes
.....
......
......
......
......
......
......
......
......
......
1
CO
NT
INU
E W
ITH
BC
9
No
......
......
......
......
......
......
......
......
......
......
2
GO
TO
SE
CT
ION
C
B
C9
Doe
s th
e co
mpu
ter/
lap
top
have
an
emai
l or
inte
rnet
con
nect
ion?
Yes
.....
......
......
......
......
......
......
......
......
......
1
No
......
......
......
......
......
......
......
......
......
......
2
SE
CT
ION
C.
US
E O
F B
ICY
CL
E
TIC
K B
OX
IF
RE
SP
ON
DE
NT
CL
EA
RL
Y U
NA
BL
E T
O R
IDE
A B
IKE
DU
E T
O A
DIS
AB
ILIT
Y:
GO
TO
SE
CT
ION
D
OT
HE
RW
ISE
CO
NT
INU
E W
ITH
C1a
. C
1a
I’d
now
like
to ta
lk to
you
abo
ut c
ycli
ng.
Hav
e yo
u cy
cled
aro
und
here
or
in L
ondo
n an
ywhe
re d
urin
g th
e la
st f
ive
year
s?
Y
es ..
......
......
......
......
......
......
......
......
......
... 1
CO
NT
INU
E W
ITH
C1b
No
......
......
......
......
......
......
......
......
......
......
2
GO
TO
SE
CT
ION
D
C1b
H
ave
you
cycl
ed a
roun
d he
re o
r in
Lon
don
anyw
here
dur
ing
the
last
12
mon
ths?
Yes
.....
......
......
......
......
......
......
......
......
......
1
CO
NT
INU
E W
ITH
C2
No
......
......
......
......
......
......
......
......
......
......
2
GO
TO
SE
CT
ION
D
C2
And
abo
ut h
ow o
ften
do
you
cycl
e ar
ound
her
e or
in L
ondo
n at
this
tim
e of
yea
r?
R
EA
D O
UT
:
Nev
er ...
......
......
......
......
......
......
......
......
.....
1
Har
dly
ever
.....
......
......
......
......
......
......
.....
2 O
nce
or tw
ice
a m
onth
.....
......
......
......
......
3
C
ON
TIN
UE
WIT
H C
3 M
ore
or le
ss e
very
wee
k ...
......
......
......
.....
4
M
ost d
ays .
......
......
......
......
......
......
......
......
5
C
3 D
o yo
u cy
cle
mor
e in
sum
mer
than
in w
inte
r?
Yes
.....
......
......
......
......
......
......
......
......
......
1
N
o ...
......
......
......
......
......
......
......
......
......
... 2
C
4 P
RO
BE
AN
D C
OD
E A
LL
ME
NT
ION
S.
For
wha
t rea
sons
do
you
cycl
e?
T
o ge
t to/
from
wor
k/sc
hool
/col
lege
etc
. .. 1
Sho
ppin
g ...
......
......
......
......
......
......
......
.....
2 L
eisu
re (
e.g.
to c
ount
ry/v
isit
frie
nds)
......
. 3
Kee
p fi
t ....
......
......
......
......
......
......
......
......
4
Rac
ing/
spor
ts ..
......
......
......
......
......
......
.....
5
C5
IF R
ES
PO
ND
EN
T S
AY
S T
HE
Y O
NL
Y S
TA
RT
ED
CY
CL
ING
3-4
YE
AR
S A
GO
, RE
PH
RA
SE
QU
ES
TIO
N
AC
CO
RD
ING
LY
. IF
ON
LY
ST
AR
TE
D C
YC
LIN
G 1
-2 Y
EA
RS
AG
O, C
IRC
LE
CO
DE
4.
Now
aday
s, d
o yo
u cy
cle
mor
e, le
ss o
r ab
out t
he s
ame
as y
ou d
id f
ive
year
s ag
o?
M
ore .
......
......
......
......
......
......
......
......
......
.. 1
Les
s ....
......
......
......
......
......
......
......
......
......
2
Abo
ut th
e sa
me
......
......
......
......
......
......
.... 3
O
nly
star
ted
cycl
ing
1-2
year
s ag
o ....
......
. 4
N
OW
GO
TO
SE
CT
ION
D
202
SE
CT
ION
D.
SE
CO
ND
HO
ME
D
1 Is
this
you
r on
ly h
ome
or d
o yo
u ha
ve a
noth
er r
esid
ence
/oth
er r
esid
ence
s/ho
lida
y ho
me(
s) in
the
UK
?
Thi
s is
my
only
hom
e ....
......
......
......
......
... 1
GO
TO
SE
CT
ION
E
I ha
ve a
noth
er r
esid
ence
in th
e U
K ..
......
.. 2
C
ON
TIN
UE
WIT
H D
2
D2
IF M
OR
E T
HA
N O
NE
, RE
CO
RD
DE
TA
ILS
OF
RE
SID
EN
CE
US
ED
MO
ST
OF
TE
N.
Wha
t is
the
addr
ess
of y
our
othe
r ho
me?
W
RIT
E I
N F
UL
L A
DD
RE
SS
DE
TA
ILS
.
Ple
ase
can
I st
art b
y as
king
:-
NU
MB
ER
/HO
US
E N
AM
E &
ST
RE
ET
TO
WN
/LO
ND
ON
AR
EA
& C
OU
NT
Y
P
OS
TC
OD
E
D3
And
whi
ch is
you
r m
ain
hom
e?
T
his
one
(i.e
. sam
ple
addr
ess)
.....
......
......
. 1
Oth
er o
ne (
i.e. a
s w
ritt
en a
bove
) ....
......
.... 2
SE
CT
ION
E.
RE
-CO
NT
AC
TS
ON
LY
AS
K E
1 IF
RE
SP
ON
DE
NT
HA
S A
DIS
AB
ILIT
Y (
CO
DE
1 A
T D
IS1)
, OT
HE
RW
ISE
GO
TO
E2.
E
1 F
rom
tim
e to
tim
e, T
rans
port
for
Lon
don
(TfL
) w
ill c
ondu
ct f
urth
er r
esea
rch
into
trav
el b
ehav
iour
am
ongs
t peo
ple
wit
h di
sabi
liti
es.
Are
you
wil
ling
to b
e co
ntac
ted
agai
n to
ans
wer
som
e fu
rthe
r qu
esti
ons?
Yes
, wil
ling
to b
e re
-con
tact
ed ..
......
......
.. 1
No,
do
not w
ant t
o be
re-
cont
acte
d ...
......
. 2
G
O T
O S
EC
TIO
N F
E2
Fro
m ti
me
to ti
me,
Tra
nspo
rt f
or L
ondo
n (T
fL)
wil
l con
duct
fur
ther
res
earc
h ab
out p
eopl
es’
trav
el
beha
viou
r. A
re y
ou w
illi
ng to
be
cont
acte
d ag
ain
to a
nsw
er s
ome
furt
her
ques
tion
s?
Y
es, w
illi
ng to
be
re-c
onta
cted
.....
......
.....
1 N
o, d
o no
t wan
t to
be r
e-co
ntac
ted
......
.... 2
GO
TO
SE
CT
ION
F
SE
CT
ION
F.
TR
IP D
ET
AIL
S O
N T
RA
VE
L D
AY
F
1 I
now
nee
d to
col
lect
info
rmat
ion
abou
t tri
ps y
ou m
ade
yest
erda
y (/
TR
AV
EL
DA
Y if
dif
fere
nt)
.
Fir
st o
f al
l, di
d yo
u le
ave
the
hous
e at
all
yes
terd
ay (
/TR
AV
EL
DA
Y if
dif
fere
nt)
?
DA
Y S
TA
RT
S A
T 4
am A
ND
EN
DS
AT
4am
TH
E F
OL
LO
WIN
G D
AY
. E
XC
LU
DE
TR
IPS
IN
TE
GR
AL
TO
JO
B -
SE
E B
A3
(e.g
. cyc
le c
ouri
er o
r bu
s/tr
ain/
taxi
/am
bula
nce
driv
er e
tc.)
.
Yes
.....
......
......
......
......
......
......
......
......
......
......
1
GO
TO
FIR
ST
TR
IP S
HE
ET
No
......
......
......
......
......
......
......
......
......
......
......
2
PR
OB
E F
UR
TH
ER
BE
FO
RE
CO
DIN
G A
S ‘
NO
’
(e.g
. “so
you
did
n’t
eve
n g
o to
th
e sh
op t
o b
uy
a p
aper
”?).
IF
CE
RT
AIN
TH
AT
NO
TR
IPS
MA
DE
, GO
TO
SE
CT
ION
J.
‘Abs
ent’
(i.e
. hou
seho
ld m
embe
r w
as
outs
ide
the
M25
for
the
enti
re T
rave
l Day
) ...
3
GO
TO
SE
CT
ION
J.
Tri
p S
hee
t C
omp
leti
on G
uid
e.
Bef
ore
star
ting
, you
sho
uld
ask
the
resp
onde
nt to
qui
ckly
run
thro
ugh
wha
t the
y di
d th
e pr
evio
us d
ay /o
n T
rave
l Day
, and
per
haps
com
plet
e th
e “M
emor
y Jo
gger
” on
the
next
pag
e. T
ell t
he r
espo
nden
t you
nee
d to
ge
t a g
ener
al p
ictu
re o
f th
eir
mov
emen
ts w
hich
wil
l in
turn
hel
p yo
u to
pro
be f
or th
e le
vel o
f de
tail
req
uire
d.
As
a gu
ide,
you
cou
ld p
rom
pt th
e tr
ip in
terc
hang
es b
y pr
obin
g w
here
dur
ing
the
trip
they
“go
t int
o or
out
of
a ve
hicl
e”, a
nd th
en a
skin
g if
they
wal
ked
from
one
to th
e ot
her.
Alw
ays
star
t wit
h th
e fi
rst t
rip
of th
e da
y, a
nd
wor
k ch
rono
logi
call
y th
roug
hout
– f
rom
4am
on
Tra
vel D
ay to
4am
on
the
next
mor
ning
.
T
RIP
= a
one
way
mov
emen
t tha
t acc
ompl
ishe
s a
purp
ose.
Get
ting
the
bus
to w
ork
is a
sin
gle
trip
, tak
ing
the
chil
dren
to s
choo
l and
then
ret
urni
ng h
ome
is 2
trip
s (i
.e. t
ake
chil
dren
to s
choo
l, go
hom
e).
Eve
ry tr
ip s
houl
d be
bro
ken
dow
n by
iden
tify
ing
the
inte
rcha
nges
. ‘R
ound
trip
s’ s
houl
d be
rec
orde
d as
2 tr
ips
(e.g
. wal
king
the
dog,
goi
ng f
or a
dri
ve in
the
coun
try
and
not s
topp
ing
anyw
here
); a
n ou
twar
d tr
ip to
the
furt
hest
poi
nt a
way
fr
om h
ome,
and
a r
etur
n tr
ip.
INT
ER
CH
AN
GE
S =
the
phys
ical
poi
nts
at w
hich
the
resp
onde
nt h
as m
oved
out
of
or in
to a
dif
fere
nt v
ehic
le.
Eac
h ve
hicl
e us
ed a
nd e
ach
wal
k at
the
begi
nnin
g, b
etw
een
diff
eren
t veh
icle
s an
d at
the
end,
all
con
stit
ute
diff
eren
t met
hods
of
tran
spor
t. T
he T
rip
She
et h
as b
een
desi
gned
to e
nsur
e w
e ge
t thi
s in
form
atio
n (m
ost
nota
bly,
peo
ple
tend
to f
orge
t abo
ut w
alk
legs
of
a tr
ip).
A
ll in
terc
hang
es m
ust b
e re
cord
ed, w
heth
er f
rom
one
met
hod
of tr
ansp
ort t
o an
othe
r, o
r to
cha
nge
from
one
bu
s/tu
be/t
rain
to a
noth
er.
The
last
cod
e fo
r T
11 o
n ea
ch tr
ip w
ill b
e A
(i.e
. arr
ived
at d
esti
nati
on).
You
onl
y ne
ed to
rec
ord
the
star
t add
ress
of
the
firs
t tri
p of
the
day
(or
tick
the
box
if h
ome
– i.e
. the
sam
ple
addr
ess)
. T
he s
tart
add
ress
for
all
sub
sequ
ent t
rips
sho
uld
be th
e de
stin
atio
n of
the
prev
ious
trip
(fo
llow
this
lo
gic
wit
h th
e re
spon
dent
and
alw
ays
chec
k).
Wri
te in
ful
l add
ress
det
ails
whe
re a
sked
, eve
n if
the
full
pos
tcod
e is
kno
wn.
Pro
mpt
for
nam
e of
off
ice/
sh
op/c
olle
ge e
tc. (
if a
ppli
cabl
e), N
umbe
r &
Str
eet N
ame,
Are
a of
Lon
don/
Tow
n, C
ount
y (i
f ap
plic
able
).
Som
e in
terc
hang
es a
re n
ot a
ddre
sses
(e.
g. a
bus
sto
p), i
n w
hich
cas
e gi
ve a
des
crip
tion
suf
fici
ent t
o id
enti
fy
the
loca
tion
as
accu
rate
ly a
s po
ssib
le (
e.g.
bus
dir
ecti
on, s
tree
t, lo
cali
ty, a
djac
ent s
hop/
pub/
offi
ce e
tc. –
“
Ele
phan
t & C
astl
e, o
utsi
de N
orth
ern
line
tube
, goi
ng to
war
ds V
auxh
all”
).
Alw
ays
use
the
24 h
our
cloc
k.
T5a
= th
e ti
me
resp
onde
nt le
ft th
eir
prev
ious
loca
tion
to tr
avel
to th
e pl
ace
wri
tten
in T
1.
T6
– be
war
e of
‘sc
hool
run
s’ (
i.e. t
here
are
usu
ally
no
chil
dren
in th
e ve
hicl
e fo
r 1
of th
e 2
trip
s w
hen
acco
mpa
nyin
g to
/fro
m s
choo
l).
Com
plet
e al
l hea
der
info
rmat
ion
accu
rate
ly o
n ea
ch T
rip
She
et.
Onc
e co
mpl
eted
, mak
e su
re th
e tr
ip n
umbe
rs (
as e
nter
ed in
(iv
)) r
un s
eque
ntia
lly
(i.e
. the
des
tina
tion
of
one
trip
is th
e or
igin
of
the
next
) an
d ch
rono
logi
call
y (i
.e. i
n co
rrec
t tim
e or
der)
acr
oss
the
day,
and
ent
er th
e to
tal
num
ber
of tr
ips
mad
e on
the
fron
t pag
e.
203
TR
IP M
EM
OR
Y J
OG
GE
R
TR
IP =
A O
NE
WA
Y M
OV
EM
EN
T T
HA
T A
CC
OM
PL
ISH
ES
A P
UR
PO
SE
IN
TE
RC
HA
NG
ES
= T
HE
PH
YS
ICA
L P
OIN
TS
AT
WH
ICH
TH
E R
ES
PO
ND
EN
T H
AS
MO
VE
D
OU
T O
F O
R I
NT
O A
DIF
FE
RE
NT
VE
HIC
LE
A
LL
IN
TE
RC
HA
NG
ES
MU
ST
BE
RE
CO
RD
ED
, WH
ET
HE
R F
RO
M O
NE
ME
TH
OD
OF
T
RA
NS
PO
RT
TO
AN
OT
HE
R, O
R T
O C
HA
NG
E F
RO
M O
NE
BU
S/T
UB
E/T
RA
IN T
O A
NO
TH
ER
C
OM
PL
ET
E A
LL
HE
AD
ER
IN
FO
RM
AT
ION
AC
CU
RA
TE
LY
ON
EA
CH
TR
IP S
HE
ET
TH
IS G
RID
CA
N B
E U
SE
D T
O R
EC
OR
D T
HE
BA
SIC
DE
TA
ILS
OF
TH
E R
ES
PO
ND
EN
T’S
TR
IPS
ON
T
RA
VE
L D
AY
.
TR
IP
NO
. D
EP
AR
TU
RE
T
IME
A
RR
IVA
L
TIM
E
DE
ST
INA
TIO
N
ME
TH
OD
(S)
OF
T
RA
NS
PO
RT
P
UR
PO
SE
TR
IP 1
TR
IP 2
TR
IP 3
TR
IP 4
TR
IP 5
TR
IP 6
TR
IP 7
TR
IP 8
TR
IP 9
TR
IP 1
0
SE
CT
ION
J.
AT
TIT
UD
INA
L Q
UE
ST
ION
S A
BO
UT
TR
AN
SP
OR
T (
3)
IF H
OU
SE
HO
LD
HA
S A
CC
ES
S T
O A
CA
R (
SE
E C
1a O
N H
HQ
), A
SK
J1,
OT
HE
RW
ISE
GO
TO
J3.
J1
PR
OB
E A
ND
CO
DE
AL
L R
EA
SO
NS
IN
CO
LU
MN
J1a
AN
D M
AIN
RE
AS
ON
IN
CO
LU
MN
J1b
.
P
eopl
e us
e ca
rs f
or a
wid
e va
riet
y of
pur
pose
s bu
t the
re a
re p
ossi
bly
only
a s
mal
l num
ber
of r
easo
ns w
hy
they
hav
e on
e in
the
firs
t pla
ce.
……
(J1a
) W
hy d
o yo
u ha
ve a
car
?
……
(J1b
) W
hat w
ould
you
say
is th
e on
e m
ain
reas
on y
ou h
ave
a ca
r?
J1a-
All
reas
ons
J1b-
Mai
n re
ason
D
on’t
rea
lly k
now
/nor
mal
thin
g to
do
/alw
ays
had
one .
..
.……
1……
.. .…
…1…
…..
Pro
vide
d by
em
ploy
er ..
......
......
......
......
......
......
......
......
.....
.…
…2.
……
. .…
…2.
……
.
Nee
d it
for
wor
k /b
usin
ess
trip
s ...
......
......
......
......
......
......
. .…
…3…
…..
.……
3……
..
Pub
lic
tran
spor
t is
poor
.....
......
......
......
......
......
......
......
......
.…
…4…
…..
.……
4……
..
Che
aper
than
pub
lic
tran
spor
t ....
......
......
......
......
......
......
...
.……
5……
.. .…
…5…
…..
To
tran
spor
t chi
ldre
n ...
......
......
......
......
......
......
......
......
......
.…
…6…
…..
.……
6……
..
For
sho
ppin
g /t
rans
port
ing
item
s ...
......
......
......
......
......
.....
.…
…7…
…..
.……
7……
..
Saf
er w
hen
trav
elli
ng a
t nig
ht ..
......
......
......
......
......
......
.....
.…
…8…
…..
.……
8……
..
Saf
er w
hen
trav
elli
ng in
this
are
a ....
......
......
......
......
......
....
.……
9……
.. .…
…9…
…..
Con
veni
ence
......
......
......
......
......
......
......
......
......
......
......
....
……
A…
….
……
A…
….
Acc
essi
bili
ty (
to p
lace
s no
t ser
ved
by p
ubli
c tr
ansp
ort)
...
……
B…
….
……
B…
….
Mob
ility
impa
ired
so
need
a c
ar ...
......
......
......
......
......
......
. …
…C
……
. …
…C
……
.
Fle
xibi
lity
(ca
n co
me
and
go to
ow
n tim
etab
le)
......
......
...
……
D…
….
……
D…
….
Hol
iday
s /w
eeke
nds
away
.....
......
......
......
......
......
......
......
..
……
E…
….
……
E…
….
To
visi
t fri
ends
/rel
ativ
es ..
......
......
......
......
......
......
......
......
.…
…F
……
.. .…
…F
……
..
Soc
ial r
easo
ns ..
......
......
......
......
......
......
......
......
......
......
.....
.…
…G
……
.. .…
…G
……
..
Oth
er (
WR
ITE
IN
) ....
......
......
......
......
......
......
......
......
......
...
.……
H…
…..
.……
H…
…..
N/A
- D
on’t
hav
e ac
cess
to c
ar /u
se c
ar
/N
ot m
y ch
oice
to g
et a
car
/giv
en it
as
pres
ent .
....
……
I……
…
…I…
…
204
J2a
CO
DE
AL
L M
EN
TIO
NS
.
(N
B:
“Fir
st t
rip”
fro
m h
ome
cou
ld b
e la
te a
fter
noo
n/e
ven
ing
if t
hat
is w
hen
resp
ond
ent
firs
t le
ft t
he
hou
se).
Thi
nkin
g ab
out t
he f
irst
trip
you
mad
e fr
om h
ome
by c
ar y
este
rday
(/T
RA
VE
L D
AY
if d
iffe
ren
t), w
hat
wou
ld y
ou h
ave
done
if a
car
had
not
bee
n av
aila
ble
that
day
? W
alk.
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
.....
1
Cyc
le ..
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
... 2
Bus
/Tra
m ..
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
. 3
Tub
e /D
LR
.....
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
... 4
Tra
in...
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
... 5
Tax
i ....
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
... 6
Ask
ed n
on-H
ouse
hold
mem
ber
for
a li
ft (
e.g.
fri
end
/wor
k co
llea
gue)
.....
......
......
......
......
. 7
Wou
ld n
ot h
ave
mad
e th
e tr
ip ...
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
8
Wou
ld h
ave
mad
e th
e tr
ip a
noth
er d
ay w
hen
car
was
ava
ilab
le ..
......
......
......
......
......
......
... 9
Wou
ld h
ave
gone
som
ewhe
re e
lse .
......
......
......
......
......
......
......
......
......
......
......
......
......
......
. A
Non
-Hou
seho
ld m
embe
r w
ould
hav
e m
ade
trip
(e.
g. o
ther
“pa
rent
” do
es s
choo
l run
) ....
.. B
Oth
er (
WR
ITE
IN
) ....
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
C
N/A
– N
o ca
r tr
ips
mad
e /d
idn’
t hav
e ac
cess
to c
ar th
at d
ay ..
......
......
......
......
......
......
......
. D
GO
TO
J3
J2b
C
OD
E A
LL
ME
NT
ION
S.
And
if a
car
was
nev
er a
vail
able
to m
ake
that
trip
, wha
t wou
ld y
ou d
o?
Tra
vel t
he s
ame
as J
2a ..
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
1
Tra
vel b
y di
ffer
ent m
eans
/com
bina
tion
of
mea
ns in
J2a
.....
......
......
......
......
......
......
......
.... 2
Wou
ld s
top
mak
ing
trip
s of
this
kin
d ...
......
......
......
......
......
......
......
......
......
......
......
......
......
3
Wou
ld w
ork
/sho
p /g
o ou
t for
leis
ure
etc.
els
ewhe
re ..
......
......
......
......
......
......
......
......
......
. 4
Wou
ld m
ove
to s
uit /
chan
ge w
here
live
.....
......
......
......
......
......
......
......
......
......
......
......
......
5
Wou
ld c
ompl
etel
y “c
hang
e li
fest
yle”
/ada
pt ..
......
......
......
......
......
......
......
......
......
......
......
.. 6
Non
-hou
seho
ld m
embe
r w
ould
mak
e tr
ip in
stea
d (e
.g. s
choo
l run
“ro
ta”)
.....
......
......
......
. 7
Oth
er (
WR
ITE
IN
) ....
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
......
8
Tri
p w
as a
‘on
e-of
f’ (
i.e. n
o ne
ed to
go
ther
e ag
ain)
.....
......
......
......
......
......
......
......
......
.....
9
AS
K A
LL
.
J3
SH
OW
CA
RD
10
O
NE
CO
DE
ON
LY
IN
EA
CH
CO
LU
MN
.
H
ow o
ften
do
you
trav
el b
y ca
r, b
ut n
ot ta
xi, a
s ei
ther
a d
rive
r or
pas
seng
er…
… (
J3a)
whe
n yo
u go
sh
oppi
ng?
… (
J3b)
whe
n yo
u go
out
for
leis
ure
pu
rpos
es (
e.g.
pub
, res
taur
ant,
cine
ma,
vis
it f
rien
ds e
tc.)
?
J3
a-S
hopp
ing
J3b
-Lei
sure
A
lway
s ...
......
......
......
......
......
......
......
......
......
......
.
.……
1……
.. .…
…1…
…..
Usu
ally
.....
......
......
......
......
......
......
......
......
......
.....
.…
…2.
……
. .…
…2.
……
.
Som
etim
es ...
......
......
......
......
......
......
......
......
......
..
.……
3……
.. .…
…3…
…..
Rar
ely
......
......
......
......
......
......
......
......
......
......
......
.…
…4…
…..
.……
4……
..
Nev
er ...
......
......
......
......
......
......
......
......
......
......
....
.…
…5.
……
. .…
…5.
……
.
N/A
- D
o no
t mak
e su
ch tr
ips .
......
......
......
......
.....
.……
6……
.. .…
…6…
…..
J4
SH
OW
CA
RD
11
O
NE
CO
DE
ON
LY
. W
hich
of
thes
e st
atem
ents
bes
t des
crib
es th
e w
ay y
ou s
hop
for
food
and
oth
er e
very
day
item
s?
I
buy
near
ly a
ll m
y fo
od a
nd o
ther
eve
ryda
y ite
ms
in s
uper
mar
kets
/hyp
erm
arke
ts ..
......
.. 1
I bu
y so
me
item
s in
sup
erm
arke
ts a
nd s
ome
in s
mal
ler
/loc
al s
hops
......
......
......
......
......
.... 2
I bu
y ne
arly
all
foo
d an
d ot
her
ever
yday
item
s in
sm
alle
r /l
ocal
sho
ps ...
......
......
......
......
... 3
Mos
t foo
d an
d ot
her
ever
yday
item
s ar
e de
live
red
here
.....
......
......
......
......
......
......
......
......
4
N/A
– S
omeo
ne e
lse
in th
e ho
useh
old
does
mos
t of t
he s
hopp
ing .
......
......
......
......
......
......
.. 5
T
HA
NK
RE
SP
ON
DE
NT
AN
D M
OV
E O
N T
O N
EX
T I
ND
IVID
UA
L I
NT
ER
VIE
W.
IF
FIN
AL
HO
US
EH
OL
D M
EM
BE
R, H
AN
D O
UT
SE
LF
-CO
MP
LE
TIO
N D
IAR
Y(I
ES
) A
ND
A-Z
.
205
206
Appendix 4 (Sample of Survey documents in 2007 for university students in Metro Manila,
Philippines: Survey cover letter and survey questionnaire)
TOKYO INSTITUTE OF TECHNOLOGY Department of Civil & Environmental Engineering
M1-11 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552 Tel : +81-3-5734-2577 Fax : +81-3-5734-3578
Survey for Travel Behavior and ICT Use
_21_March 2007 Dear Professors, staffs and students: The survey is funded by Japanese Society for the Promotion of Science (JSPS) Core University Program (Trilateral Collaborative Research Program among TokyoTech, University of the Philippines and Kasetsart University). Your participation in completing this questionnaire will help in understanding the relationship of Information and Communication Technology (ICT) use to travel behavior and social activities. This questionnaire will be used only for academic purposes. Please answer the questions to the best of your knowledge and make sure you do not miss any of the questions. It will only take approximately 10 minutes to complete this questionnaire. The information that you provide is assured to be confidential. Thank you very much for your time and support. Very truly yours,
Dr. Daisuke Fukuda Associate Professor Civil and Environmental Engineering Tokyo Institute of Technology Mailing Address: M1-11, 2-12-1, O-okayama, Meguro-ku, Tokyo 152-8552, Japan Phone: +81-3-5734-2577 Fax: +81-3-5734-3578 Email: [email protected]
Grace U. Padayhag Research Student Civil and Environmental Engineering Tokyo Institute of Technology Mailing Address: M1-11, 2-12-1, O-okayama, Meguro-ku, Tokyo 152-8552, Japan Phone: +81-80-5090-0677 Email: [email protected]
207
1.N
ame:
___
____
____
___(
optio
nal)
2. A
ge:_
____
____
_ 3.
Gen
der:
M
ale
Fem
ale
4. S
tatu
s:
Si
ngle
Mar
ried
5.
Num
ber o
f yrs
/mos
. of s
tay
in th
e
pres
ent r
esid
ence
: __
____
year
s _
____
_mon
ths
6. F
amily
size
:___
____
____
____
_ 7.
Pre
sent
loca
tion,
City
:___
____
____
__
8. O
ccup
atio
n:__
____
____
____
__
9. C
ompa
ny/S
choo
l:___
____
____
10
. Typ
e of
com
pany
/sch
ool:
Gov
ernm
ent
Pri
vate
11
. Num
ber o
f yea
rs w
orki
ng o
r st
udyi
ng:
1-3
yea
rs
16-2
0 4-
6
21-2
5
7-
9
26-3
0 10
-15
>
30
12. E
duca
tiona
l atta
inm
ent:
Voca
tiona
l edu
catio
n Ba
chel
or d
egre
e M
aste
r’s d
egre
e D
octo
rate
deg
ree
Post
-doc
tora
l deg
ree
13. I
ncom
e pe
r mon
th a
llow
ance
in
peso
s:
600
0-90
00
250
00-2
9000
10
000-
1400
0
3
0000
-340
00
1500
0-19
000
350
00-3
9000
20
000-
2400
0
>
4000
0 14
. Car
ow
ners
hip:
N
one
1
2
3
>
4 15
. Hou
se o
wne
rshi
p
D
orm
insi
de c
ampu
s
Dor
m o
utsi
de c
ampu
s
Apar
tmen
t
Con
dom
iniu
m
Pa
rent
’s h
ouse
Bed
spac
e
Rela
tive’
s hou
se
ow
ned
1. D
o yo
u ow
n a
cell
phon
e?
ye
s
no
2. If
no ,
wha
t mak
es y
ou re
sist
ant i
n ac
quiri
ng
cell
phon
e?
fam
ily m
embe
rs h
ave
mob
ile p
hone
al
read
y us
ed in
tern
et in
stea
d us
ed la
ndlin
e ph
ones
inst
ead
expe
nsiv
e do
n’t w
ant t
o ge
t con
tact
ed b
y m
obile
ph
ones
n
ot a
pplic
able
(N/A
) 3.
How
man
y ce
ll ph
ones
do
you
own?
1
2
3
>4
N/A
4.
W
hat n
etw
ork
are
you
usin
g?
G
lobe
Touc
h m
obile
N/A
Smar
t
Talk
and
text
Sun
cellu
lar
Addi
ct m
obile
5.
W
hat t
ype
of p
lan
is y
our c
ell p
hone
?
Pre-
paid
regu
lar m
onth
ly p
lan
6.
If y
ou h
ave
mor
e th
an o
ne c
ell p
hone
, are
all
cell
phon
es in
the
sam
e m
obile
net
wor
k?
Ye
s
no
N/A
7.
If
you
hav
e m
ore
than
one
cel
l pho
ne, d
oes
each
pho
ne y
ou o
wne
d ha
ve th
e sa
me
set o
f co
ntac
t num
bers
?
Yes
no
N
/A
8.
If y
ou h
ave
sepa
rate
d co
ntac
t num
bers
in
each
cel
l pho
ne, w
hat i
s the
mai
n re
ason
of
havi
ng se
para
ting
cont
act n
umbe
rs fr
om o
ne
phon
e to
ano
ther
? D
iffer
ent s
et o
f fri
ends
for
ever
y ne
twor
k Fa
mily
pho
ne
Less
cos
t in
send
ing
text
m
essa
ges w
ith
the
sam
e n
etw
ork
Com
pany
/bus
ines
s pho
ne
N/A
9.
H
ow m
any
fam
ily m
embe
rs w
ho h
ave
cell
phon
es?
1-
3
7-
9
N/A
4-6
>10
10.
Do
your
fam
ily m
embe
rs a
nd fr
iend
s ha
ve th
e sa
me
netw
ork
as y
ou?
Yes
no
N/A
11
. Wha
t is t
he p
urpo
se o
f ow
ning
cel
l
phon
e an
d its
freq
uenc
y of
us ?
Bu
sine
ss…
……
..……
.___
___
Wor
k-re
late
d Pe
rson
al…
……
…..…
.___
___
Scho
olw
ork,
St
udie
s and
rese
arch
H
obby
and
am
usem
ent_
____
_ So
cial
life
……
……
..…__
____
N
/A
12. I
f yo
u ha
ve
mor
e th
an
one
cell
phon
e, w
hat
is t
he p
urpo
se o
f ow
ning
m
ore
than
one
MP?
Bu
sine
ss
Com
mun
icat
ion
Pers
onal
To
con
tact
frie
nds w
ho a
re in
the
sam
e ne
twor
k (e
.g. g
lobe
to g
lobe
) Ju
st c
anno
t dis
pose
the
cell
phon
eN
/A
13.
How
man
y of
you
r fr
iend
s do
hav
e m
obile
pho
ne?
0-19
10
0-14
9
20-3
9
15
0-19
9 40
-59
200-
300
60-7
9
30
1-40
0 80
-99
>
401
14.
Sinc
e w
hen
did
you
own
a ce
ll ph
one?
19
99
20
02
20
05
N/A
20
00
20
03
20
06
20
01
20
04
20
07
14. D
o yo
u of
ten
trave
l bef
ore
you
owne
d a
mob
ile
phon
e?
alw
ays
so
met
imes
ra
rely
nev
er
15
. Wha
t do
you
usua
lly d
o be
fore
whe
n yo
u ha
ve
no c
ell p
hone
yet
/ als
o fo
r tho
se w
ho h
ave
no
mob
ile p
hone
up
to n
ow?
(mul
tiple
ans
wer
s OK
) St
ay h
ome
Read
boo
ks
Wat
ch T
V
Play
spor
ts
Inte
rnet
St
udy
Do
hous
ehol
d ch
ores
Vi
sit r
elat
ives
and
frie
nds
Go
recr
eatio
nal p
lace
s (pa
rks,
beac
hes,
etc.
) Su
rf th
e In
tern
et
List
en to
mus
ic
Play
“ga
me
and
wat
ch”/
“pla
ysta
tion”
16
. Wha
t are
met
hods
of c
omm
unic
atin
g so
meo
ne o
r be
fore
hav
ing
a m
obile
pho
ne?
Le
tter
(mul
tiple
ans
wer
s OK
) La
ndlin
e ca
ll Te
legr
am
Fax
Inte
rnet
M
essa
ge re
lay
by c
lose
frie
nds a
nd re
lativ
es
17
. Wha
t typ
e of
pla
ce d
o yo
u go
usi
ng c
ell p
hone
or
even
for t
hose
a w
ithou
t cel
l pho
ne y
et?
Mal
l
(mul
tiple
ans
wer
s OK
) Re
stau
rant
/ cof
fee
hous
e Sc
hool
W
ork
plac
e H
ome
Bus s
tatio
ns
Trai
n st
atio
ns
Alon
g th
e st
reet
(P
ublic
) Mar
ket p
lace
C
ivic
/ pr
ofes
sion
al O
rgan
izat
ion’
s offi
ce
18. A
re y
ou a
war
e of
usi
ng g
roup
-sen
ding
feat
ure
in
your
mob
ile p
hone
?
Ye
s
no
N/A
19
. If y
es, a
re y
ou u
sing
it to
mak
e on
e-tim
e-se
ndin
g m
essa
ges?
Ye
s
no
N/A
2. C
ell P
hone
Use
1.
Soc
io-d
emog
raph
ics
Writ
e th
e nu
mbe
r on
the
box
prov
ided
fo
r the
freq
uenc
y of
usi
ng th
e ce
ll ph
one:
1.
1-
4 tim
e a
day
6
. 25-
29
2.
5-9
7
. 30-
34
3.
10-1
4
8.
35-
39
4.
15-1
9
9.
40-
44
5.
20-2
4
10.
>45
208
3. P
erce
ptio
ns/A
ttitu
des
(Ple
ase
enci
rcle
the
emot
e ic
on w
hich
you
thin
k is
the
mos
t app
ropr
iate
.) St
rong
ly a
gree
Agre
e N
eutr
al
Dis
agre
eSt
rong
ly D
isag
ree
No
idea
1.
To w
hat e
xten
t do
you
agre
e th
at th
e us
e of
cel
l pho
ne e
ncou
rage
s you
to tr
avel
?
2.
To
wha
t ext
ent d
o yo
u ag
ree
that
the
use
of c
ell p
hone
enc
oura
ges y
ou to
mak
e m
ore
frie
nds?
3.
To
wha
t ext
ent d
o yo
u ag
ree
that
the
use
of c
ell p
hone
mak
es y
ou fe
el sa
fe a
nd se
cure
?
4.
To
wha
t ext
ent d
o yo
u ag
ree
that
send
ing
text
mes
sage
s thr
ough
cel
l pho
nes i
s mor
e pr
actic
al
than
voi
ce c
allin
g?
5. D
o yo
u se
nd m
essa
ges t
hrou
gh m
obile
pho
ne b
ecau
se y
ou ju
st w
ant s
omeo
ne to
talk
to?
6. D
o yo
u co
nsid
er th
at h
avin
g an
mob
ile p
hone
is
Impo
rtan
t
C
omfo
rtab
le
Relia
ble
Che
ap
Con
veni
ent
St
ylis
h an
d Fa
shio
nabl
e
Effe
ctiv
e co
mm
unic
atio
n
7.
How
do
you
feel
whe
n se
ndin
g te
xt m
essa
ges t
o yo
ur fr
iend
s?
Hap
py
Insp
ired
Ex
cite
d
Ir
rita
ted
Inte
rest
ed
8. H
ow d
o yo
u us
ually
feel
whe
n yo
u re
ceiv
e te
xt m
essa
ges?
H
appy
In
spir
ed
Exci
ted
Irri
tate
d
In
tere
sted
GAD
S! T
his
is
emba
rras
sing
! “Y
es J
ayso
n,
you
may
go
to
the
lava
tory
, bu
t ne
xt t
ime
just
rai
se y
our
hand
.”
209
4. S
ocia
l Act
iviti
es/ L
ife st
yle.
(Enc
ircl
e th
e nu
mbe
r or
the
lett
er w
hich
you
thin
k is
the
mos
t app
ropr
iate
for
you)
1. T
ype
of
“int
erac
tions
” En
circ
le th
e le
tter f
or th
e av
erag
e fr
eque
ncie
s of t
he in
tera
ctio
n:
Enci
rcle
the
num
ber
for t
he n
umbe
r of
pers
ons y
ou c
onta
cted
:Fo
r who
m u
sual
ly th
e in
tera
ctio
n is
for?
Th
e av
erag
e du
ratio
n of
th
e ca
ll or
the
leng
th o
f th
e em
ail o
r let
ter:
Ran
k th
e ty
pe o
f in
tera
ctio
n w
hich
you
use
as
med
ia in
dis
cuss
ing
impo
rtant
mat
ters
(1
bei
ng th
e m
ost m
edia
us
ed a
nd 8
bei
ng th
e le
ast
med
ia u
sed)
:
(1) S
endi
ng
Text
mes
sage
s
a. se
vera
l tim
es a
day
e
. on
ce a
wee
k
b.
onc
e a
day
f.
2 p
er m
onth
c.
twic
e a
day
g.
onc
e a
mon
th
d. t
wic
e a
wee
k
h. o
nce
a ye
ar
a. 0
-4
f. 25
-29
b.
5-9
g.
30-
34
c. 1
0-14
h
. 35-
39
d. 1
5-19
i
. 40-
44
e. 2
0-24
j
. >45
1. F
amily
mem
bers
5. n
ot so
clo
se fr
iend
s2.
Imm
edia
te re
lativ
es 6
. ext
ende
d fr
iend
s 3.
clo
se fr
iend
s 4.
col
leag
ues
1. 1
-79
char
acte
rs
2. 8
0- 1
60 c
hara
cter
s 3.
50
wor
ds
4. 1
00 w
ords
5.
>15
0 w
ords
(2) T
alke
d
by c
ell p
hone
a. se
vera
l tim
es a
day
e
. on
ce a
wee
k
b.
onc
e a
day
f.
2 p
er m
onth
c.
twic
e a
day
g.
onc
e a
mon
th
d. t
wic
e a
wee
k
h. o
nce
a ye
ar
a. 0
-4
f. 25
-29
b.
5-9
g.
30-
34
c. 1
0-14
h
. 35-
39
d. 1
5-19
i
. 40-
44
e. 2
0-24
j
. >45
1. F
amily
mem
bers
5. n
ot so
clo
se fr
iend
s2.
Imm
edia
te re
lativ
es 6
. ext
ende
d fr
iend
s 3.
clo
se fr
iend
s 4.
col
leag
ues
1.1-
29 m
inut
es
2.
30m
ins –
1ho
ur
3. 1
- 3ho
urs
4.
I da
y
(3) T
alke
d
by la
ndlin
e
a. se
vera
l tim
es a
day
e
. on
ce a
wee
k
b.
onc
e a
day
f.
2 p
er m
onth
c.
twic
e a
day
g.
onc
e a
mon
th
d. t
wic
e a
wee
k
h. o
nce
a ye
ar
a. 0
-4
f. 25
-29
b.
5-9
g.
30-
34
c. 1
0-14
h
. 35-
39
d. 1
5-19
i
. 40-
44
e. 2
0-24
j
. >45
1. F
amily
mem
bers
5. n
ot so
clo
se fr
iend
s2.
Imm
edia
te re
lativ
es 6
. ext
ende
d fr
iend
s 3.
clo
se fr
iend
s 4.
col
leag
ues
1.1-
29 m
inut
es
2.
30m
ins –
1ho
ur
3. 1
- 3ho
urs
4.
I da
y
(4) T
alke
d
in p
erso
n
a. se
vera
l tim
es a
day
e
. on
ce a
wee
k
b.
onc
e a
day
f.
2 p
er m
onth
c.
twic
e a
day
g.
onc
e a
mon
th
d. t
wic
e a
wee
k
h. o
nce
a ye
ar
a. 0
-4
f. 25
-29
b.
5-9
g.
30-
34
c. 1
0-14
h
. 35-
39
d. 1
5-19
i
. 40-
44
e. 2
0-24
j
. >45
1. F
amily
mem
bers
5. n
ot so
clo
se fr
iend
s2.
Imm
edia
te re
lativ
es 6
. ext
ende
d fr
iend
s 3.
clo
se fr
iend
s 4.
col
leag
ues
1.1-
29 m
inut
es
2.
30m
ins –
1 h
our
3. 1
- 3ho
urs
4.
I da
y
(5) S
end
an
em
ail
a. se
vera
l tim
es a
day
e
. on
ce a
wee
k
b.
onc
e a
day
f.
2 p
er m
onth
c.
twic
e a
day
g.
onc
e a
mon
th
d. t
wic
e a
wee
k
h. o
nce
a ye
ar
a. 0
-4
f. 25
-29
b.
5-9
g.
30-
34
c. 1
0-14
h
. 35-
39
d. 1
5-19
i
. 40-
44
e. 2
0-24
j
. >45
1. F
amily
mem
bers
5. n
ot so
clo
se fr
iend
s2.
Imm
edia
te re
lativ
es 6
. ext
ende
d fr
iend
s 3.
clo
se fr
iend
s 4.
col
leag
ues
1. 1
-79
char
acte
rs
2. 8
0- 1
60 c
hara
cter
s 3.
50
wor
ds
4. 1
00 w
ords
5.
>15
0 w
ords
(6) S
end
an
inst
ant
mes
sage
a. se
vera
l tim
es a
day
e
. on
ce a
wee
k
b.
onc
e a
day
f.
2 p
er m
onth
c.
twic
e a
day
g.
onc
e a
mon
th
d. t
wic
e a
wee
k
h. o
nce
a ye
ar
a. 0
-4
f. 25
-29
b.
5-9
g.
30-
34
c. 1
0-14
h
. 35-
39
d. 1
5-19
i
. 40-
44
e. 2
0-24
j
. >45
1. F
amily
mem
bers
5. n
ot so
clo
se fr
iend
s2.
Imm
edia
te re
lativ
es 6
. ext
ende
d fr
iend
s 3.
clo
se fr
iend
s 4.
col
leag
ues
1. 1
-79
char
acte
rs
2. 8
0- 1
60 c
hara
cter
s 3.
50
wor
ds
4. 1
00 w
ords
5.
>15
0 w
ords
(7) C
onta
cted
by
lette
r
a. se
vera
l tim
es a
day
e
. on
ce a
wee
k
b.
onc
e a
day
f.
2 p
er m
onth
c.
twic
e a
day
g.
onc
e a
mon
th
d. t
wic
e a
wee
k
h. o
nce
a ye
ar
a. 0
-4
f. 25
-29
b.
5-9
g.
30-
34
c. 1
0-14
h
. 35-
39
d. 1
5-19
i
. 40-
44
e. 2
0-24
j
. >45
1. F
amily
mem
bers
5. n
ot so
clo
se fr
iend
s2.
Imm
edia
te re
lativ
es 6
. ext
ende
d fr
iend
s 3.
clo
se fr
iend
s 4.
col
leag
ues
1. 1
-79
char
acte
rs
2. 8
0- 1
60 c
hara
cter
s 3.
50
wor
ds
4. 1
00 w
ords
5.
>15
0 w
ords
(8) G
et a
cqua
inte
d in
rest
aura
nts o
r an
y pu
blic
pla
ces
a. se
vera
l tim
es a
day
e
. on
ce a
wee
k
b.
onc
e a
day
f.
2 p
er m
onth
c.
twic
e a
day
g.
once
a m
onth
d.
tw
ice
a w
eek
h
. onc
e a
year
a. 0
-4
f. 25
-29
b.
5-9
g.
30-
34
c. 1
0-14
h
. 35-
39
d. 1
5-19
i
. 40-
44
e. 2
0-24
j
. >45
1. F
amily
mem
bers
5. n
ot so
clo
se fr
iend
s2.
Imm
edia
te re
lativ
es 6
. ext
ende
d fr
iend
s 3.
clo
se fr
iend
s 4.
col
leag
ues
1.1-
29 m
inut
es
2.
30m
ins –
1ho
ur
3.
1- 3
hour
s
4. I
day
210
2. T
ype
of
“Soc
ial A
ctiv
ities
”
Enci
rcle
the
num
ber
on th
e fir
st c
olum
n fo
r the
freq
uenc
y of
pe
rfor
min
g th
e so
cial
ac
tiviti
es?
Enci
rcle
the
num
ber o
n th
e se
cond
col
umn
of
wha
t typ
e of
veh
icle
you
us
ually
use
d in
doi
ng
such
soci
al a
ctiv
ities
?
Enci
rcle
the
num
ber o
n th
e th
ird c
olum
n of
ho
w m
any
num
ber o
f fr
iend
s you
us
ually
do
the
soci
al a
ctiv
ity?
Enci
rcle
the
num
ber o
n th
e fo
urth
col
umn
of
how
is th
e so
cial
ac
tivity
bei
ng
plan
ned?
Enci
rcle
the
num
ber o
n th
e fif
th c
olum
n of
W
ith w
hom
is
the
activ
ity
usua
lly
perf
orm
ed?
(sin
o an
g ka
sam
a)
Enci
rcle
e th
e nu
mbe
r on
the
sixt
h co
lum
n of
to
who
m is
the
activ
ity u
sual
ly
perf
orm
ed?
(p
ara
kani
no a
ng
soci
al a
tivity
)
Wha
t tim
e do
you
us
ually
do
the
soci
al
activ
ities
?
Enci
rcle
the
num
ber o
n th
e si
xth
colu
mn
of
wha
t spe
cific
lo
catio
n do
you
us
ually
per
form
so
cial
act
iviti
es?
(1) S
hopp
ing
1.
Ever
y da
y
2.
2 pe
r day
3
. 1
per w
eek
4.
2 pe
r wee
k
5
. 3
per w
eek
6.
I per
mon
th
7.
2 pe
r mon
th
8
. 3
per m
onth
9
. 1
per
yea
r 10
. 2
per y
ear
11.
3 pe
r yea
r 12
. Ev
ery
2 ye
ars
13. E
very
3 y
ears
14
. Nev
er
1.
priv
ate
car
2.
je
epne
y
3.
tr
icyc
le/p
edic
ad
4.
Taxi
5.
FX
6.
Ai
rcon
d bu
s 7.
N
on-a
irco
nd b
us
8.
MRT
trai
n
9.
Non
e, B
y w
alk
1. a
lone
2.
tw
o 3.
gro
up o
f 3
4. g
roup
of 4
5.
gro
up o
f 5
6. 9
–12
7. >
12
8. >
25
9. >
80
10.
>15
0
1. a
t the
inst
ant
2. 3
0 m
inut
es
3. 1
hou
r 4.
hou
rs
5. a
day
6.
a w
eek
7. a
mon
th
8. a
yea
r ahe
ad
1. f
amily
mem
ber
2. i
mm
edia
tely
rela
tives
3.
fri
ends
4.
cla
ssm
ates
5.
offi
cem
ates
6.
civ
ic a
nd
pr
ofes
sion
al
or
gani
zatio
n
m
ates
7.
nei
ghbo
rs
8. A
lone
1. f
amily
mem
ber
2. i
mm
edia
tely
rela
tives
3.
fri
ends
4.
cla
ssm
ates
5.
offi
cem
ates
6.
civ
ic a
nd
pr
ofes
sion
al
or
gani
zatio
n
m
ates
7.
nei
ghbo
rs
8. S
elf
1. 8
-12a
m
2. 1
-4pm
3.
5-9
pm
4. 1
0pm
onw
ards
1. m
all
2. c
hurc
h 3.
mov
ie h
ouse
4.
arc
ades
or
ente
rtai
nmen
t pa
rlor
5.
int
erne
t ca
fe
6. c
offe
e sh
op
7. p
rovi
nce
8. h
ouse
9.
gym
nasi
um
10.
offic
e 11
. re
stau
rant
s
(2) D
inne
r w
ith fr
iend
s
1.
Ever
y da
y
2.
2 pe
r day
3
. 1
per w
eek
4.
2 pe
r wee
k
5
. 3
per w
eek
6.
I per
mon
th
7.
2 pe
r mon
th
8
. 3
per m
onth
9
. 1
per
yea
r 10
. 2
per y
ear
11.
3 pe
r yea
r 12
. Ev
ery
2 ye
ars
13. E
very
3 y
ears
14
. Nev
er
1. p
riva
te c
ar
2. je
epne
y
3.tr
icyc
le/p
edic
ad
4.Ta
xi
5.FX
6.
Airc
ond
bus
7.N
on-a
irco
nd b
us
8.
MRT
trai
n
9Non
e, B
y w
alk
1. a
lone
2.
tw
o 3.
gro
up o
f 3
4. g
roup
of 4
5.
gro
up o
f 5
6. 9
– 1
2 7.
>12
8.
>25
9.
>80
10
. >
150
1. a
t the
inst
ant
2. 3
0 m
inut
es
3. 1
hou
r 4.
hou
rs
5. a
day
6.
a w
eek
7. a
mon
th
8. a
yea
r ahe
ad
1. f
amily
mem
ber
2. i
mm
edia
tely
rela
tives
3.
fri
ends
4.
cla
ssm
ates
5.
offi
cem
ates
6.
civ
ic a
nd
pr
ofes
sion
al
or
gani
zatio
n
m
ates
7.
nei
ghbo
rs
8. A
lone
1. f
amily
mem
ber
2. i
mm
edia
tely
rela
tives
3.
fri
ends
4.
cla
ssm
ates
5.
offi
cem
ates
6.
civ
ic a
nd
pr
ofes
sion
al
or
gani
zatio
n
m
ates
7.
nei
ghbo
rs
8. S
elf
1. 8
-12a
m
2. 1
-4pm
3.
5-9
pm
4. 1
0pm
onw
ards
1. m
all
2. c
hurc
h 3.
mov
ie h
ouse
4.
arc
ades
or
ente
rtai
nmen
t pa
rlor
5.
int
erne
t ca
fe
6. c
offe
e sh
op
7. p
rovi
nce
8. h
ouse
9.
gym
nasi
um
10.
offic
e 11
. re
stau
rant
s
(3) V
isit
pa
rent
s’ p
lace
1.
Ever
y da
y
2.
2 pe
r day
3
. 1
per w
eek
4.
2 pe
r wee
k
5
. 3
per w
eek
6.
I per
mon
th
7.
2 pe
r mon
th
8
. 3
per m
onth
9
. 1
per
yea
r 10
. 2
per y
ear
11.
3 pe
r yea
r 12
. Ev
ery
2 ye
ars
13. E
very
3 y
ears
14
. Nev
er
1. p
riva
te c
ar
2. je
epne
y
3.tr
icyc
le/p
edic
ad
4.Ta
xi
5.FX
6.
Airc
ond
bus
7.N
on-a
irco
nd b
us
8.
MRT
trai
n
9Non
e, B
y w
alk
1. a
lone
2.
tw
o 3.
gro
up o
f 3
4. g
roup
of 4
5.
gro
up o
f 5
6. 9
– 1
2 7.
>12
8.
>25
9.
>80
10
. >
150
1. a
t the
inst
ant
2. 3
0 m
inut
es
3. 1
hou
r 4.
hou
rs
5. a
day
6.
a w
eek
7. a
mon
th
8. a
yea
r ahe
ad
1. f
amily
mem
ber
2. i
mm
edia
tely
rela
tives
3.
fri
ends
4.
cla
ssm
ates
5.
offi
cem
ates
6.
civ
ic a
nd
pr
ofes
sion
al
or
gani
zatio
n
m
ates
7.
nei
ghbo
rs
8. A
lone
1. f
amily
mem
ber
2. i
mm
edia
tely
rela
tives
3.
fri
ends
4.
cla
ssm
ates
5.
offi
cem
ates
6.
civ
ic a
nd
pr
ofes
sion
al
or
gani
zatio
n
m
ates
7.
nei
ghbo
rs
8. S
elf
1. w
eeke
nds
2. h
olid
ays
3. a
ny ti
me
of th
e
wee
k 4.
wee
kday
s
1. m
all
2. c
hurc
h 3.
mov
ie h
ouse
4.
arc
ades
or
ente
rtai
nmen
t pa
rlor
5.
int
erne
t ca
fe
6. c
offe
e sh
op
7. p
rovi
nce
8. h
ouse
9.
gym
nasi
um
10.
offic
e 11
. re
stau
rant
s
211
(4) V
isit
re
lativ
es’ p
lace
1.
Ever
y da
y
2.
2 pe
r day
3
. 1
per w
eek
4.
2 pe
r wee
k
5
. 3
per w
eek
6.
I per
mon
th
7.
2 pe
r mon
th
8
. 3
per m
onth
9
. 1
per
yea
r 10
. 2
per y
ear
11.
3 pe
r yea
r 12
. Ev
ery
2 ye
ars
13. E
very
3 y
ears
14
. Nev
er
1. p
riva
te c
ar
2. je
epne
y
3.tr
icyc
le/p
edic
ad
4.Ta
xi
5.FX
6.
Airc
ond
bus
7.N
on-a
irco
nd b
us
8.
MRT
trai
n
9Non
e, B
y w
alk
1. a
lone
2.
tw
o 3.
gro
up o
f 3
4. g
roup
of 4
5.
gro
up o
f 5
6. 9
– 1
2 7.
>12
8.
>25
9.
>80
10
. >
150
1. a
t the
inst
ant
2. 3
0 m
inut
es
3. 1
hou
r 4.
hou
rs
5. a
day
6.
a w
eek
7. a
mon
th
8. a
yea
r ahe
ad
1. fa
mily
mem
ber
2. i
mm
edia
tely
re
lativ
es
3. f
rien
ds
4. c
lass
mat
es
5. o
ffice
mat
es
6. c
ivic
and
pr
ofes
sion
al
orga
niza
tion
mat
es
7. n
eigh
bors
8.
Alo
ne
1. f
amily
mem
ber
2. i
mm
edia
tely
rela
tives
3.
fri
ends
4.
cla
ssm
ates
5.
offi
cem
ates
6.
civ
ic a
nd
pr
ofes
sion
al
or
gani
zatio
n
m
ates
7.
nei
ghbo
rs
8. S
elf
1. w
eeke
nds
2. h
olid
ays
3. a
ny ti
me
of th
e
wee
k 4.
wee
kday
s
1. m
all
2. c
hurc
h 3.
mov
ie h
ouse
4.
arc
ades
or
ente
rtai
nmen
t pa
rlor
5.
int
erne
t ca
fe
6. c
offe
e sh
op
7. p
rovi
nce
8. h
ouse
9.
gym
nasi
um
10.
offic
e 11
. re
stau
rant
s
(5) V
isit
fr
iend
s’ p
lace
1.
Ever
y da
y
2.
2 pe
r day
3
. 1
per w
eek
4.
2 pe
r wee
k
5
. 3
per w
eek
6.
I per
mon
th
7.
2 pe
r mon
th
8
. 3
per m
onth
9
. 1
per
yea
r 10
. 2
per y
ear
11.
3 pe
r yea
r 12
. Ev
ery
2 ye
ars
13. E
very
3 y
ears
14
. Nev
er
1. p
riva
te c
ar
2. je
epne
y
3.tr
icyc
le/p
edic
ad
4.Ta
xi
5.FX
6.
Airc
ond
bus
7.N
on-a
irco
nd b
us
8.
MRT
trai
n
9Non
e, B
y w
alk
1. a
lone
2.
tw
o 3.
gro
up o
f 3
4. g
roup
of 4
5.
gro
up o
f 5
6. 9
– 1
2 7.
>12
8.
>25
9.
>80
10
. >
150
1. a
t the
inst
ant
2. 3
0 m
inut
es
3. 1
hou
r 4.
hou
rs
5. a
day
6.
a w
eek
7. a
mon
th
8. a
yea
r ahe
ad
1. fa
mily
mem
ber
2. i
mm
edia
tely
re
lativ
es
3. f
rien
ds
4. c
lass
mat
es
5. o
ffice
mat
es
6. c
ivic
and
pr
ofes
sion
al
orga
niza
tion
mat
es
7. n
eigh
bors
8.
Alo
ne
1. f
amily
mem
ber
2. i
mm
edia
tely
rela
tives
3.
fri
ends
4.
cla
ssm
ates
5.
offi
cem
ates
6.
civ
ic a
nd
pr
ofes
sion
al
or
gani
zatio
n
m
ates
7.
nei
ghbo
rs
8.
Self
1. w
eeke
nds
2. h
olid
ays
3. a
ny ti
me
of th
e
wee
k 4.
wee
kday
s
1. m
all
2. c
hurc
h 3.
mov
ie h
ouse
4.
arc
ades
or
ente
rtai
nmen
t pa
rlor
5.
int
erne
t ca
fe
6. c
offe
e sh
op
7. p
rovi
nce
8. h
ouse
9.
gym
nasi
um
10.
offic
e 11
. re
stau
rant
s
(6) W
atch
mov
ies
1.
Ever
y da
y
2.
2 pe
r day
3
. 1
per w
eek
4.
2 pe
r wee
k
5
. 3
per w
eek
6.
I per
mon
th
7.
2 pe
r mon
th
8
. 3
per m
onth
9
. 1
per
yea
r 10
. 2
per y
ear
11.
3 pe
r yea
r 12
. Ev
ery
2 ye
ars
13. E
very
3 y
ears
14
. Nev
er
1. p
riva
te c
ar
2. je
epne
y
3.tr
icyc
le/p
edic
ad
4.Ta
xi
5.FX
6.
Airc
ond
bus
7.N
on-a
irco
nd b
us
8.
MRT
trai
n
9Non
e, B
y w
alk
1. a
lone
2.
tw
o 3.
gro
up o
f 3
4. g
roup
of 4
5.
gro
up o
f 5
6. 9
– 1
2 7.
>12
8.
>25
9.
>80
10
. >
150
1. a
t the
inst
ant
2. 3
0 m
inut
es
3. 1
hou
r 4.
hou
rs
5. a
day
6.
a w
eek
7. a
mon
th
8. a
yea
r ahe
ad
1. fa
mily
mem
ber
2. i
mm
edia
tely
re
lativ
es
3. f
rien
ds
4. c
lass
mat
es
5. o
ffice
mat
es
6. c
ivic
and
pr
ofes
sion
al
orga
niza
tion
mat
es
7. n
eigh
bors
8.
Alo
ne
1. f
amily
mem
ber
2. i
mm
edia
tely
rela
tives
3.
fri
ends
4.
cla
ssm
ates
5.
offi
cem
ates
6.
civ
ic a
nd
pr
ofes
sion
al
or
gani
zatio
n
m
ates
7.
nei
ghbo
rs
8.
Self
1. 8
-12a
m
2. 1
-4pm
3.
5-9
pm
4. 1
0pm
onw
ards
1. m
all
2. c
hurc
h 3.
mov
ie h
ouse
4.
arc
ades
or
ente
rtai
nmen
t pa
rlor
5.
int
erne
t ca
fe
6. c
offe
e sh
op
7. p
rovi
nce
8. h
ouse
9.
gym
nasi
um
10.
offic
e 11
. re
stau
rant
s
212
(7) W
atch
co
ncer
t/spo
rts
1.
Ever
y da
y
2.
2 pe
r day
3.
1 pe
r wee
k
4
. 2
per w
eek
5.
3 pe
r wee
k
6
. I p
er m
onth
7
. 2
per m
onth
8.
3 pe
r mon
th
9.
1 p
er y
ear
10.
2 pe
r yea
r 11
. 3
per y
ear
12.
Ever
y 2
year
s 13
. Eve
ry 3
yea
rs
14. N
ever
1. p
riva
te c
ar
2. je
epne
y
3.tr
icyc
le/p
edic
ad
4.Ta
xi
5.FX
6.
Airc
ond
bus
7.N
on-a
irco
nd b
us
8.M
RT tr
ain
9N
one,
By
wal
k
1. a
lone
2.
tw
o 3.
gro
up o
f 3
4. g
roup
of 4
5.
gro
up o
f 5
6. 9
– 1
2 7.
>12
8.
>25
9.
>80
10
. >
150
1. a
t the
inst
ant
2. 3
0 m
inut
es
3. 1
hou
r 4.
hou
rs
5. a
day
6.
a w
eek
7. a
mon
th
8. a
yea
r ahe
ad
1. fa
mily
mem
ber
2. i
mm
edia
tely
re
lativ
es
3. f
rien
ds
4. c
lass
mat
es
5. o
ffice
mat
es
6. c
ivic
and
pr
ofes
sion
al
orga
niza
tion
mat
es
7. n
eigh
bors
8.
Alo
ne
1. f
amily
mem
ber
2. i
mm
edia
tely
rela
tives
3.
fri
ends
4.
cla
ssm
ates
5.
offi
cem
ates
6.
civ
ic a
nd
pr
ofes
sion
al
or
gani
zatio
n
m
ates
7.
nei
ghbo
rs
8. S
elf
1. 8
pm
2. 9
pm
3. 1
0pm
onw
ards
1. m
all
2. c
hurc
h 3.
mov
ie h
ouse
4.
arc
ades
or
ente
rtai
nmen
t pa
rlor
5.
int
erne
t ca
fe
6. c
offe
e sh
op
7. p
rovi
nce
8. h
ouse
9.
gym
nasi
um
10.
offic
e 11
. re
stau
rant
s
(8) F
iest
a ce
lebr
atio
ns
1.
Ever
y da
y
2.
2 pe
r day
3.
1 pe
r wee
k
4
. 2
per w
eek
5.
3 pe
r wee
k
6
. I p
er m
onth
7
. 2
per m
onth
8.
3 pe
r mon
th
9.
1 p
er y
ear
10.
2 pe
r yea
r 11
. 3
per y
ear
12.
Ever
y 2
year
s 13
. Eve
ry 3
yea
rs
14. N
ever
1. p
riva
te c
ar
2. je
epne
y
3.tr
icyc
le/p
edic
ad
4.Ta
xi
5.FX
6.
Airc
ond
bus
7.N
on-a
irco
nd b
us
8.M
RT tr
ain
9N
one,
By
wal
k
1. a
lone
2.
tw
o 3.
gro
up o
f 3
4. g
roup
of 4
5.
gro
up o
f 5
6. 9
– 1
2 7.
>12
8.
>25
9.
>80
10
. >
150
1. a
t the
inst
ant
2. 3
0 m
inut
es
3. 1
hou
r 4.
hou
rs
5. a
day
6.
a w
eek
7. a
mon
th
8. a
yea
r ahe
ad
1. fa
mily
mem
ber
2. i
mm
edia
tely
re
lativ
es
3. f
rien
ds
4. c
lass
mat
es
5. o
ffice
mat
es
6. c
ivic
and
pr
ofes
sion
al
orga
niza
tion
mat
es
7. n
eigh
bors
8.
Alo
ne
1. f
amily
mem
ber
2. i
mm
edia
tely
rela
tives
3.
fri
ends
4.
cla
ssm
ates
5.
offi
cem
ates
6.
civ
ic a
nd
pr
ofes
sion
al
or
gani
zatio
n
m
ates
7.
nei
ghbo
rs
8. S
elf
1. w
eeke
nds
2. h
olid
ays
3. a
ny ti
me
of th
e
wee
k 4.
wee
kday
s
1. m
all
2. c
hurc
h 3.
mov
ie h
ouse
4.
arc
ades
or
ente
rtai
nmen
t pa
rlor
5.
int
erne
t ca
fe
6. c
offe
e sh
op
7. p
rovi
nce
8. h
ouse
9.
gym
nasi
um
10.
offic
e 11
. re
stau
rant
s
(9) O
ut-o
f-to
wn
vaca
tion
with
fr
iend
s
1.
Ever
y da
y
2.
2 pe
r day
3.
1 pe
r wee
k
4
. 2
per w
eek
5.
3 pe
r wee
k
6
. I p
er m
onth
7
. 2
per m
onth
8.
3 pe
r mon
th
9.
1 p
er y
ear
10.
2 pe
r yea
r 11
. 3
per y
ear
12.
Ever
y 2
year
s 13
. Eve
ry 3
yea
rs
14. N
ever
1. p
riva
te c
ar
2. je
epne
y
3.tr
icyc
le/p
edic
ad
4.Ta
xi
5.FX
6.
Airc
ond
bus
7.N
on-a
irco
nd b
us
8.M
RT tr
ain
9N
one,
By
wal
k
1. a
lone
2.
tw
o 3.
gro
up o
f 3
4. g
roup
of 4
5.
gro
up o
f 5
6. 9
– 1
2 7.
>12
8.
>25
9.
>80
10
. >
150
1. a
t the
inst
ant
2. 3
0 m
inut
es
3. 1
hou
r 4.
hou
rs
5. a
day
6.
a w
eek
7. a
mon
th
8. a
yea
r ahe
ad
1. fa
mily
mem
ber
2. i
mm
edia
tely
re
lativ
es
3. f
rien
ds
4. c
lass
mat
es
5. o
ffice
mat
es
6. c
ivic
and
pr
ofes
sion
al
orga
niza
tion
mat
es
7. n
eigh
bors
8.
Alo
ne
1. f
amily
mem
ber
2. i
mm
edia
tely
rela
tives
3.
fri
ends
4.
cla
ssm
ates
5.
offi
cem
ates
6.
civ
ic a
nd
pr
ofes
sion
al
or
gani
zatio
n
m
ates
7.
nei
ghbo
rs
8. S
elf
1. w
eeke
nds
2. h
olid
ays
3. a
ny ti
me
of th
e
wee
k 4.
wee
kday
s
1. m
all
2. c
hurc
h 3.
mov
ie h
ouse
4.
arc
ades
or
ente
rtai
nmen
t pa
rlor
5.
int
erne
t ca
fe
6. c
offe
e sh
op
7. p
rovi
nce
8. h
ouse
9.
gym
nasi
um
10.
offic
e 11
. re
stau
rant
s
(10)
Aff
iliat
ion
m
eetin
gs
1.
Ever
y da
y
2.
2 pe
r day
3.
1 pe
r wee
k
4
. 2
per w
eek
5.
3 pe
r wee
k
6
. I p
er m
onth
7
. 2
per m
onth
8.
3 pe
r mon
th
9.
1 p
er y
ear
10.
2 pe
r yea
r 11
. 3
per y
ear
12.
Ever
y 2
year
s 13
. Eve
ry 3
yea
rs
14. N
ever
1. p
riva
te c
ar
2. je
epne
y
3.tr
icyc
le/p
edic
ad
4.Ta
xi
5.FX
6.
Airc
ond
bus
7.N
on-a
irco
nd b
us
8.M
RT tr
ain
9N
one,
By
wal
k
1. a
lone
2.
tw
o 3.
gro
up o
f 3
4. g
roup
of 4
5.
gro
up o
f 5
6. 9
– 1
2 7.
>12
8.
>25
9.
>80
10
. >
150
1. a
t the
inst
ant
2. 3
0 m
inut
es
3. 1
hou
r 4.
hou
rs
5. a
day
6.
a w
eek
7. a
mon
th
8. a
yea
r ahe
ad
1. fa
mily
mem
ber
2. i
mm
edia
tely
re
lativ
es
3. f
rien
ds
4. c
lass
mat
es
5. o
ffice
mat
es
6. c
ivic
and
pr
ofes
sion
al
orga
niza
tion
mat
es
7. n
eigh
bors
8.
Alo
ne
1. f
amily
mem
ber
2. i
mm
edia
tely
rela
tives
3.
fri
ends
4.
cla
ssm
ates
5.
offi
cem
ates
6.
civ
ic a
nd
pr
ofes
sion
al
or
gani
zatio
n
m
ates
7.
nei
ghbo
rs
8. S
elf
1. 8
-12a
m
2. 1
-4pm
3.
5-9
pm
4. 1
0pm
onw
ards
1. m
all
2. c
hurc
h 3.
mov
ie h
ouse
4.
arc
ades
or
ente
rtai
nmen
t pa
rlor
5.
int
erne
t ca
fe
6. c
offe
e sh
op
7. p
rovi
nce
8. h
ouse
9.
gym
nasi
um
10.
offic
e 11
. re
stau
rant
s
213
3. W
hat a
re o
ther
med
ia y
ou u
se in
mak
ing
soci
al a
ctiv
ities
? Pe
rson
al c
ompu
ter…
…__
___
Palm
top/
PDA…
……
…__
___
Land
line
pho
ne…
……
____
_ Te
levi
sion
……
……
……
____
_ In
tern
et u
se…
……
……
____
_ O
ther
s, sp
ecify
……
……
____
_ 1.
W
hat t
ime
do y
ou u
sual
ly le
ave
your
7-7:
30am
8-
8:30
9-
9:30
10
-10:
30
hom
e?
4. If
you
ans
wer
ed #
2 w
ith p
rivat
e ca
r, ho
w m
uch
do y
ou u
sual
ly p
ay fo
r the
ga
solin
e co
st p
er d
ay?
100
peso
s/da
y 20
0 pe
sos/
day
300
peso
s/da
y 40
0 pe
sos/
day
500
peso
s/da
y O
ther
s, sp
ecify
____
____
____
5.
If y
ou a
nsw
ered
# 2
with
pub
lic
utili
ty v
ehic
les,
how
muc
h do
you
us
ually
spen
d fo
r the
fare
? 10
0 pe
sos/
day
200
peso
s/da
y 30
0 pe
sos/
day
400
peso
s/da
y 50
0 pe
sos/
day
Oth
ers,
spec
ify__
____
____
__
6.
D
o yo
u w
alk
to a
nea
rby
wai
ting
stat
ion
to h
ail f
or p
ublic
util
ity
vehi
cle
(PU
V)?
Yes
no
7.
How
man
y m
inut
es w
alk
from
you
r ho
use
to th
e w
aitin
g ar
ea?
0-3
min
s 4-
6 m
ins
7-10
min
s 11
-15
min
s 15
-18
min
s 19
-22
min
s O
ther
s, sp
ecify
____
____
__
8.
How
man
y ho
urs t
rave
l tim
e fr
om h
ome
to
scho
ol o
r wor
k pl
ace?
10
min
s
1hr a
nd 2
0min
s 15
min
s
1.5
hr
30m
ins
1
hr a
nd 4
5 m
ins
45m
ins
2h
rs
Oth
ers,
spec
ify _
____
____
_ 9.
D
o yo
u us
e m
obile
pho
ne w
hile
on
the
way
(in
tran
sit)
to sc
hool
or w
orkp
lace
?
Yes
no
som
etim
es w
hen
urge
nt
Look
che
etah
! A
tex
t m
essa
ge
from
Tar
zan
2.
Wha
t is t
he p
rimar
y m
ode
of tr
ansp
ort a
re y
ou u
sing
whe
n yo
u go
to sc
hool
or
wor
k?
C
heck
the
mod
e
C
heck
the
Ran
ge
Writ
e th
e fr
eque
ncy
of u
se
Jeep
ney
pe
r day
_
____
____
___
Tric
ycle
/ ped
icab
per w
eek
FX
pe
r mon
th
Taxi
MRT
trai
n
Priv
ate
car
Re
gula
r bus
Ai
r con
bus
N
one,
by
wal
king
O
ther
s, sp
ecify
___
____
____
____
5.Tr
avel
beha
vior
Freq
uenc
ies o
f usi
ng o
ther
Info
rmat
ion
and
com
mun
icat
ion
tech
nolo
gy (I
CT)
: 1
. Ev
ery
day
7. 1
per
yea
r
2
. 2
per d
ay
8
. 2 p
er y
ear
3.
1 pe
r wee
k
9. 3
per
yea
r 4
. 2
per w
eek
10.
Eve
ry 2
yea
rs
5.
I per
mon
th
1
1. E
very
3 y
ears
6
. 2
per m
onth
12.
Nev
er
3.
Wha
t is t
he se
cond
ary
mod
e of
tran
spor
t are
you
usi
ng w
hen
you
go to
scho
ol
or w
ork?
C
heck
the
mod
e
(Mul
tiple
ans
wer
s OK
)
Che
ck in
the
box
the
pref
erre
d us
e of
tra
nspo
rt Pe
r day
p
er w
eek
p
er m
onth
Writ
e th
e fr
eque
ncy
of u
se
on th
e sp
ace
prov
ided
Jeep
ney
Tr
icyc
le/ p
edic
ab
FX
Taxi
M
RT tr
ain
Priv
ate
car
Regu
lar b
us
Ai
r con
bus
Non
e, b
y w
alki
ng
O
ther
spec
ify
214
10. W
hat i
s the
usu
al ti
me-
in in
scho
ol o
r in
wor
k pl
ace?
7a
m
8am
9a
m
10am
Fl
exib
le w
orki
ng ti
me
Oth
ers,
spec
ify__
____
____
11
. Wha
t tim
e do
you
usu
al b
e at
hom
e?
4-5p
m
6-7
8-9
10-1
1 O
ther
s, sp
ecify
____
____
_ 12
. Whe
n yo
u ar
e at
hom
e, d
o yo
u us
e yo
ur
mob
ile p
hone
?
Yes
no
som
etim
es w
hen
urge
nt 13
. Wha
t is t
he u
sual
tim
e-ou
t in
scho
ol o
r w
ork
plac
e?
4pm
5-
6
7-8
9-10
O
ther
s, sp
ecify
____
___
14. A
fter w
ork
or sc
hool
wha
t is t
he u
sual
ac
tivity
you
do?
(Mul
tiple
ans
wer
s OK
)Sh
oppi
ng
Cof
fee
or h
ango
ut w
ith fr
iend
s
Wat
ch m
ovie
s O
ut-o
f-hom
e di
nner
W
atch
con
cert
St
udy
Go
to in
tern
et c
afé
Gro
cery
Fe
tch
kids
from
scho
ol
Fetc
h fr
iend
s fro
m o
ther
15.
How
man
y rid
es d
o yo
u us
ually
hav
e in
goi
ng to
wor
k pl
ace
or sc
hool
? O
nce
Twic
e Th
rice
4
times
5
times
or m
ore
16
. H
ow m
any
rides
do
you
usua
lly h
ave
in
goin
g ba
ck h
ome?
(d
o no
t for
get t
o co
nsid
er th
e rid
es
whe
n yo
u go
shop
ping
afte
r wor
k)
Onc
e Tw
ice
Thri
ce
4 tim
es
5 tim
es o
r mor
e 17
. If y
ou re
ceiv
e a
mes
sage
from
you
r fr
iend
s whi
ch re
quire
you
to v
isit
them
, ho
w lo
ng a
re y
ou w
illin
g to
trav
el fo
r th
em?
30m
ins
2hrs
45
min
s
>
2.5r
s 1
hr
>3h
rs
1.5h
rs
not w
illin
g
MA
RA
MIN
G S
AL
AM
AT
!!!
A
RIG
AT
OU
GO
ZA
IMA
SHIT
A!!
!
TH
AN
K Y
OU
VE
RY
MU
CH
!!!
We
hope
to c
ontin
ue th
is ty
pe o
f sur
vey
next
tim
e.
If in
tere
sted
, ple
ase
writ
e yo
ur e
mai
l add
ress
:
We
wis
h to
mee
t and
con
tact
you
aga
in in
our
futu
re re
sear
ches
.
215
Nam
e of
frien
ds (fo
r sm
all se
rvices
, lik
e mga
nag
papa
uang
)
1.__
____
____
____
____
____
__
11._
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____
__
21._
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____
__
2.__
____
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____
____
____
__
12._
____
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____
____
____
__
22._
____
____
____
____
____
__
3.__
____
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____
____
__
13._
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____
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____
__
23._
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____
____
__
4.__
____
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____
__
14._
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____
____
____
__
24._
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____
____
__
5.__
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____
____
____
__
15._
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____
__
25._
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____
__
6.__
____
____
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____
__
16._
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____
____
____
__
26._
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____
____
__
7.__
____
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____
____
____
__
17._
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____
____
__
27._
____
____
____
____
____
__
8.__
____
____
____
____
____
__
18._
____
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____
____
____
__
28._
____
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____
____
__
9.__
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____
__
19._
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____
__
29._
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____
__
10._
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____
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____
__
20._
____
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____
____
____
__
30._
____
____
____
____
____
__
Age
Pl
ease
wri
te t
he i
n th
e fi
rst
box
prov
ided
the
nu
mbe
r th
at c
orre
spon
ds t
o th
e ag
e ra
nge
of y
our
frie
nd
Relation
ship
Pl
ease
wri
te t
he i
n th
e se
cond
box
pro
vide
d th
e le
tter
tha
t co
rres
pond
s wi
th y
our
rela
tion
to
your
fri
end.
1.
15
-20
2.
21
-25
3.
26
-30
4.
31
-35
5.
36
-40
6.
>4
0
a.
Fam
ily m
embe
r
b.
Rel
ativ
e
c.
Frie
nd
d.
Acq
uain
tanc
e
e.
Col
leag
ue/o
ffic
emat
es/a
ffili
atio
n m
ates
f.
Cla
ssm
ate
Aba
h! S
yem
pz!
Ilis
ta m
o la
ng a
ng m
ga p
abor
ito
mon
g ka
ibig
an o
kay
a ka
ibig
an p
ero
“not
so
clos
e”
ika
nga
sa E
nglis
h. M
ay 3
0 bl
ank
spac
es
pero
di k
inak
aila
ngan
g pu
nuin
ito.
Kun
g ila
ng f
rien
ds la
ng a
ng m
eron
ka
bawa
t ca
tego
ry y
un la
ng a
ng is
usul
at m
o. T
anda
an
ang
apat
na
cate
gori
es n
g ka
ibig
an: a
ng
kaib
igan
for
impo
rtan
t pe
rson
al m
atte
rs,
kaib
igan
for
soc
ializ
ing,
kai
biga
n fo
r ad
vice
an
d ka
ibig
an f
or s
mal
l ser
vice
s lik
e m
ga
nagp
apau
tang
na
mga
fri
ends
.
May
par
ang
sam
ple
ballo
t fo
rm k
asi a
ko
para
pan
g-pr
acti
ce n
g pa
gsus
ulat
sa
balo
ta.
Gust
o m
ong
sagu
tan?
mad
ali l
ang
ito,
pu
hhhr
amis
!
Hi A
nna!
Mal
apit
na
elek
siyo
n no
? Re
ady
ka n
a bu
mut
o?
sige
nga
…pwe
de b
ang
isus
ulat
ko
yon
g m
ga k
aibi
gan
at m
ga
ka-ib
igan
ko?
Heh
ehe
joke
joke
Oo
ba! A
no b
a an
g ga
gawi
n di
yan?
Hel
lo J
ohn!
Oo
nga
lapi
t na
el
eksi
yon.
Bat
pal
a na
i-kwe
nto
ang
tung
kol s
a el
eksi
yon?
N
A
M
E G E N
E R A
T O
R S
Mar
amin
g sa
lam
at!!!
Ari
gato
u G
ozai
mas
hit
a!!!
Th
ank
you
ver
y m
uch
!!!
4. F
rom
tim
e to
tim
e pe
ople
bor
row
som
ethi
ng f
rom
oth
er
peop
le,
for
inst
ance
, a
smal
l su
m o
f m
oney
, or
a p
iece
of
equi
pmen
t, o
r as
k fo
r he
lp w
ith
smal
l jo
bs i
n or
aro
und
the
hous
e.
Plea
se e
nter
nam
es o
f th
ese
peop
le in
bel
ow o
pen
spac
es.
216
Nam
e of
frien
ds (fo
r im
port
ant
mat
ters
)
1.__
____
____
____
____
____
__
11._
____
____
____
____
____
__
21._
____
____
____
____
____
__
2.__
____
____
____
____
____
__
12._
____
____
____
____
____
__
22._
____
____
____
____
____
__
3.__
____
____
____
____
____
__
13._
____
____
____
____
____
__
23._
____
____
____
____
____
__
4.__
____
____
____
____
____
__
14._
____
____
____
____
____
__
24._
____
____
____
____
____
__
5.__
____
____
____
____
____
__
15._
____
____
____
____
____
__
25._
____
____
____
____
____
__
6.__
____
____
____
____
____
__
16._
____
____
____
____
____
__
26._
____
____
____
____
____
__
7.__
____
____
____
____
____
__
17._
____
____
____
____
____
__
27._
____
____
____
____
____
__
8.__
____
____
____
____
____
__
18._
____
____
____
____
____
__
28._
____
____
____
____
____
__
9.__
____
____
____
____
____
__
19._
____
____
____
____
____
__
29._
____
____
____
____
____
__
10._
____
____
____
____
____
__
20._
____
____
____
____
____
__
30._
____
____
____
____
____
__
Nam
e of
frien
ds (fo
r so
cializing)
1.
____
____
____
____
____
____
11
.___
____
____
____
____
____
21
.___
____
____
____
____
____
2.__
____
____
____
____
____
__
12._
____
____
____
____
____
__
22._
____
____
____
____
____
__
3.__
____
____
____
____
____
__
13._
____
____
____
____
____
__
23._
____
____
____
____
____
__
4.__
____
____
____
____
____
__
14._
____
____
____
____
____
__
24._
____
____
____
____
____
__
5.__
____
____
____
____
____
__
15._
____
____
____
____
____
__
25._
____
____
____
____
____
__
6.__
____
____
____
____
____
__
16._
____
____
____
____
____
__
26._
____
____
____
____
____
__
7.__
____
____
____
____
____
__
17._
____
____
____
____
____
__
27._
____
____
____
____
____
__
8.__
____
____
____
____
____
__
18._
____
____
____
____
____
__
28._
____
____
____
____
____
__
9.__
____
____
____
____
____
__
19._
____
____
____
____
____
__
29._
____
____
____
____
____
__
10._
____
____
____
____
____
__
20._
____
____
____
____
____
__
30._
____
____
____
____
____
__
Nam
e of
frien
ds (fo
r ad
vice
) 1.
____
____
____
____
____
____
11
.___
____
____
____
____
____
21
.___
____
____
____
____
____
2.__
____
____
____
____
____
__
12._
____
____
____
____
____
__
22._
____
____
____
____
____
__
3.__
____
____
____
____
____
__
13._
____
____
____
____
____
__
23._
____
____
____
____
____
__
4.__
____
____
____
____
____
__
14._
____
____
____
____
____
__
24._
____
____
____
____
____
__
5.__
____
____
____
____
____
__
15._
____
____
____
____
____
__
25._
____
____
____
____
____
__
6.__
____
____
____
____
____
__
16._
____
____
____
____
____
__
26._
____
____
____
____
____
__
7.__
____
____
____
____
____
__
17._
____
____
____
____
____
__
27._
____
____
____
____
____
__
8.__
____
____
____
____
____
__
18._
____
____
____
____
____
__
28._
____
____
____
____
____
__
9.__
____
____
____
____
____
__
19._
____
____
____
____
____
__
29._
____
____
____
____
____
__
10._
____
____
____
____
____
__
20._
____
____
____
____
____
__
30._
____
____
____
____
____
__
Age
Pl
ease
wri
te t
he i
n th
e fi
rst
box
prov
ided
the
nu
mbe
r th
at c
orre
spon
ds t
o th
e ag
e ra
nge
of y
our
frie
nd
Relation
ship
Pl
ease
wri
te t
he i
n th
e se
cond
box
pro
vide
d th
e le
tter
tha
t co
rres
pond
s wi
th y
our
rela
tion
to
your
fri
end.
1.
15
-20
2.
21
-25
3.
26
-30
4.
31
-35
5.
36
-40
6.
>4
0
a.
Fam
ily m
embe
r
b.
Rel
ativ
e
c.
Frie
nd
d.
Acq
uain
tanc
e
e.
Col
leag
ue/o
ffic
emat
es/a
ffili
atio
n m
ates
f.
Cla
ssm
ate
2. S
omet
imes
you
soc
ializ
e wi
th o
ther
indi
vidu
als,
for
exa
mpl
e,
you
visi
t th
em (o
r th
ey v
isit
you
), yo
u ta
ke v
acat
ion
toge
ther
or
go t
o di
nner
, mov
ies,
etc
. Who are those people you
usually socialize with?
Plea
se w
rite
the
nam
es o
f th
ese
peop
le in
the
pro
vide
d sp
aces
.
1. “F
rom
tim
e to
tim
e pe
ople
tal
k ab
out
impo
rtan
t pe
rson
al m
atte
rs w
ith
othe
r pe
ople
, for
inst
ance
is t
hey
have
pro
blem
s at
wor
k, a
t sc
hool
, wit
h th
eir
pare
nts
or in
oth
er r
elat
ed s
itua
tion
s. W
ho a
re t
he p
eople
with
who
m y
ou d
iscu
ss
pers
onal m
atte
rs t
hat
are
impo
rtan
t to
you
?"
Nam
e on
ly s
ome
sign
ific
ant
pers
ons.
Arr
ange
of
the
nam
es is
not
impo
rtan
t
3. F
rom
tim
e to
tim
e pe
ople
ask
oth
er p
eopl
e fo
r ad
vice
whe
n a
maj
or c
hang
e oc
curs
in t
heir
life
(for
inst
ance
sel
ecti
ng a
m
ajor
, a j
ob c
hang
e or
som
ethi
ng s
imila
r).
Plea
se w
rite
the
nam
es o
f th
ese
peop
le in
the
pro
vide
d sp
aces
217
218
Appendix 5 (Sample of Survey documents in 2008 for university workers in Metro Manila,
Philippines: Survey cover letter and survey questionnaire)
TOKYO INSTITUTE OF TECHNOLOGY Department of Civil & Environmental Engineering
M1-11 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552 Tel : +81-3-5734-2577 Fax : +81-3-5734-3578
Survey for Travel Behavior and ICT Use
10 March 2008 Dear Professors and staffs: The survey is funded by Japanese Society for the Promotion of Science (JSPS) Core University Program (Trilateral Collaborative Research Program among TokyoTech, University of the Philippines and Kasetsart University). Your participation in completing this questionnaire will help in understanding the relationship of Information and Communication Technology (ICT) use to travel behavior and social activities. This questionnaire will be used only for academic purposes. Please answer the questions to the best of your knowledge and make sure you do not miss any of the questions. It will only take approximately 20 minutes to complete this questionnaire. The information that you provide is assured to be confidential. Thank you very much for your time and support. Very truly yours,
Dr. Daisuke Fukuda Associate Professor Civil and Environmental Engineering Tokyo Institute of Technology Mailing Address: M1-11, 2-12-1, O-okayama, Meguro-ku, Tokyo 152-8552, Japan Phone: +81-3-5734-2577 Fax: +81-3-5734-3578 Email: [email protected]
Grace U. Padayhag Research Student Civil and Environmental Engineering Tokyo Institute of Technology Mailing Address: M1-11, 2-12-1, O-okayama, Meguro-ku, Tokyo 152-8552, Japan Phone: +81-80-5090-0677 Email: [email protected]
219
1.N
ame:
___
____
____
____
____
____
____
___
2. A
ge:_
____
____
_ 3.
Gen
der:
M
ale
Fem
ale
4. S
tatu
s:
Si
ngle
Mar
ried
5.
Num
ber o
f yrs
/mos
. of s
tay
in th
e
pres
ent r
esid
ence
: __
____
year
s _
____
_mon
ths
6. F
amily
size
:___
____
____
____
_ 7.
Pre
sent
loca
tion,
City
:___
____
____
__
8. O
ccup
atio
n:__
____
____
____
__
9. C
ompa
ny/S
choo
l:___
____
____
10
. Typ
e of
com
pany
/sch
ool:
Gov
ernm
ent
Pri
vate
11
. Num
ber o
f yea
rs w
orki
ng a
t pre
sent
job:
___
____
12
. Edu
catio
nal a
ttain
men
t: Vo
catio
nal e
duca
tion
Bach
elor
deg
ree
Mas
ter’
s deg
ree
Doc
tora
te d
egre
e Po
st-d
octo
ral d
egre
e 13
. Inc
ome
per m
onth
in p
esos
: 6
000-
1000
0
2
5000
-300
00
1000
0-15
000
� 3
0000
0
15
000-
2000
0
20
000-
2500
0
14
. Car
ow
ners
hip:
0
1
2
3
>
4 15
. Hou
se o
wne
rshi
p
Ap
artm
ent /
Con
dom
iniu
m
Pa
rent
’s h
ouse
/ ow
ned
Be
d sp
ace
Re
lativ
e’s h
ouse
16
. H
ow m
any
cell
phon
es d
o yo
u ow
n?
0
1
2
3
>
4
17.
Wha
t net
wor
k ar
e yo
u us
ing?
G
lobe
Touc
h m
obile
N/A
Sm
art
Ta
lk a
nd te
xt
Sun
cellu
lar
Addi
ct m
obile
18
. W
hat t
ype
of p
lan
is y
our c
ell p
hone
?
Pre-
paid
regu
lar m
onth
ly p
lan
19.
How
man
y fa
mily
mem
bers
who
hav
e ce
ll ph
ones
?
1-3
7-9
N
/A
4-
6
>
10
1.
Did
you
ofte
n tra
vel b
efor
e yo
u ha
ve a
mob
ile
phon
e?
alw
ays
so
met
imes
ra
rely
nev
er
2.
Now
that
you
hav
e ce
ll ph
one
do y
ou u
se to
trav
el
mor
e of
ten?
al
way
s
som
etim
es
rare
ly
n
ever
3.
W
hat
is
the
purp
ose
of
owni
ng
cell
phon
e an
d its
freq
uenc
y of
use
?
Bu
sine
ss/ W
ork-
rela
ted…
……
…._
____
_
Pers
onal
……
……
……
……
.……
.___
___
Hob
by a
nd S
ocia
l life
……
….…
..___
___
4. W
hat a
re o
ther
med
ia y
ou u
se in
mak
ing
soci
al
activ
ities
? (M
A)
Pers
onal
com
pute
r……
....…
____
_ Pa
lm to
p/PD
A……
……
….…
____
_ La
nd li
ne p
hone
……
……
..…__
___
Tele
visi
on…
……
……
……
.…__
___
Inte
rnet
use
……
……
…..…
.…__
___
Oth
ers,
spec
ify…
……
…...
…__
___
4. W
hat a
re m
etho
ds o
f com
mun
icat
ing
som
eone
or
befo
re h
avin
g a
mob
ile p
hone
?
(Mul
tiple
ans
wer
s are
OK
) Le
tter
Land
line
call
Tele
gram
Fa
x In
tern
et
Mes
sage
rela
y by
fam
ily m
embe
rs a
nd fr
iend
s
5. W
hat t
ype
of p
lace
do
you
go u
sing
cel
l pho
ne o
r ev
en fo
r tho
se a
with
out c
ell p
hone
yet
? (M
ultip
le a
nsw
ers a
re O
K)
Mal
l
Re
stau
rant
/ cof
fee
hous
e Sc
hool
W
ork
plac
e/ c
ivic
org
aniz
atio
n’s o
ffice
H
ome
Bus s
tatio
ns/tr
ain
stat
ions
Al
ong
the
stre
et
(Pub
lic) M
arke
t pla
ce
6. W
hat d
o yo
u us
ually
do
befo
re w
hen
you
have
no
cell
phon
e ye
t/ al
so fo
r tho
se w
ho h
ave
no
mob
ile p
hone
up
to n
ow?
(mul
tiple
ans
wer
s OK
)Pa
ssiv
e A
ctiv
ities
: Re
ad b
ooks
/mag
azin
es/n
ewsp
aper
s W
atch
TV
Li
sten
to m
usic
Pl
ay “
gam
e an
d w
atch
”/“p
lays
tatio
n”
Sur
f the
Inte
rnet
A
ctiv
e A
ctiv
ities
: Pl
ay sp
orts
/go
to g
ym
Atte
nd p
artie
s/ce
lebr
atio
ns/e
vent
s D
o ho
useh
old
chor
es
Visi
t rel
ativ
es a
nd fr
iend
s G
o re
crea
tiona
l pla
ces (
park
s, be
ache
s, et
c.)
1. W
hat i
s the
usu
al ti
me
do y
ou le
ave
your
hom
e?
7-73
0am
9-9:
30am
7:30
-8
9:30
-10a
m o
nwar
ds
8-
830
8:
30-9
am s
1. S
ocio
-dem
ogra
phic
s
2. IC
T us
e an
d R
eact
ive
Act
iviti
es
Writ
e th
e nu
mbe
r on
the
spac
e pr
ovid
ed fo
r the
fr
eque
ncy
of u
sing
the
cell
phon
e pe
r day
: 1.
1-
4
6. 2
5-29
11. N
ever
2.
5-9
7
. 30-
34
3.
10-1
4
8.
35-
39
4.
15-1
9
9.
40-
44
5.20
-24
10
. >45
3. T
rave
l beh
avio
r
2. W
hat i
s the
prim
ary
mod
e* of t
rans
port
you
use
whe
n yo
u go
to sc
hool
or w
ork?
*P
rimar
y m
ode
is th
e ve
hicl
e w
ith lo
nges
t dis
tanc
e tra
vele
d fr
om h
ome
to w
ork
plac
e.
#isa
ng c
heck
lang
sa v
ehic
le a
t isa
ng c
heck
din
sa ra
nge
at is
ulat
kun
g ila
ng b
eses
(bal
ikan
) gi
naga
mit
per d
ay, p
er w
eek
or p
er m
onth
sa sp
ace.
Che
ck th
e m
ode#
Che
ck th
e R
ange
#
W
rite
the
freq
uenc
y of
use
#
Jeep
ney
pe
r day
_
____
____
___
Tric
ycle
/ ped
icab
per w
eek
FX
pe
r mon
th
Taxi
MRT
trai
n
Priv
ate
car
Re
gula
r bus
Ai
r con
bus
N
one,
by
wal
king
O
ther
s, sp
ecify
___
____
____
____
Look
ch
eeta
h!
Wri
te th
e fr
eque
ncie
s of u
sing
oth
er
info
rmat
ion
and
com
mun
icat
ion
tech
nolo
gy
(IC
T) in
the
spac
e pr
ovid
ed:
1.
�5 d
ay
5. 1
-4 p
er m
onth
2
. 1
-4 d
ay
6
. N
ever
3
. 1-
4 w
eek
4.
�5 w
eek
220
3. If
you
ans
wer
ed in
# 2
with
priv
ate
car,
how
m
uch
do y
ou u
sual
ly p
ay fo
r the
gas
olin
e co
st p
er
day?
____
____
____
peso
s 4.
If y
ou a
nsw
ered
in #
2 w
ith p
ublic
util
ity
vehi
cles
, how
muc
h do
you
usu
ally
spen
d fo
r th
e fa
re p
er d
ay?
___
____
____
_pes
os
10. D
o yo
u us
e m
obile
pho
ne w
hile
on
the
way
(in
trans
it or
insi
de th
e ve
hicl
e) to
wor
kpla
ce?
Ye
s no
so
met
imes
whe
n ur
gent
11
. H
ow m
any
rides
do
you
usua
lly h
ave
in g
oing
to
wor
k pl
ace
or sc
hool
? O
nce
only
2
times
3
times
4
times
or m
ore
12. W
hat i
s the
usu
al ti
me-
out i
n w
ork
plac
e?
4-4:
30pm
6:
30-7
pm
4:30
-5pm
7-
7:30
pm
5- 5
:30p
m
7:
30-8
pm
5:30
-6pm
8-
8:30
pm
6-6:
30pm
8:
30-9
pm o
nwar
ds
O
ther
s, sp
ecify
____
___
13. A
fter w
ork
wha
t is t
he u
sual
act
ivity
you
do?
(M
ultip
le a
nsw
ers a
re O
K)
Shop
ping
/ gro
cery
C
offe
e or
han
gout
with
frie
nds
W
atch
mov
ies/
Wat
ch c
once
rt
Out
-of-h
ome
dinn
er
Go
to in
tern
et c
afé
ot
hers
, spe
cify
____
____
14
. H
ow m
any
hour
s tra
vel t
o sh
oppi
ng fr
om
wor
kpla
ce?
less
than
30m
ins
30m
ins-
1hou
r gr
eate
r tha
n 1
hour
no
t app
licab
le(N
/A)
othe
rs, s
peci
fy__
____
__
15.
How
man
y rid
es d
o yo
u us
ually
hav
e in
goi
ng b
ack
hom
e?
(do
not f
orge
t to
cons
ider
the
rides
whe
n yo
u go
sh
oppi
ng a
fter w
ork)
O
nce
only
2
times
3
times
4
times
or m
ore
16. I
f you
rece
ive
a m
essa
ge fr
om y
our f
riend
s who
re
quire
you
to v
isit
them
, how
long
are
you
will
ing
to tr
avel
for t
hem
? 30
min
s
2h
rs
45m
ins
>2.
5rs
1 hr
>
3hrs
1.
5hrs
no
t will
ing
� �
� o
�
� �
6.
How
man
y ho
urs t
rave
l tim
e fr
om h
ome
to w
ork
plac
e?
usin
g pr
imar
y m
ode:
5-
10m
ins
45
min
s -1h
r 10
-20m
ins
1h
r 20
-30m
ins
g
reat
er th
an 1
hr
30-4
5min
s
Oth
ers,
spec
ify _
____
____
_ us
ing
seco
ndar
y m
ode:
5-
10m
ins
45
min
s -1h
r 10
-20m
ins
1h
r 20
-30m
ins
g
reat
er th
an 1
hr
30-4
5min
s
Oth
ers,
spec
ify _
____
____
_
7.
Do
you
wal
k to
a n
earb
y w
aitin
g st
atio
n to
ha
il fo
r pub
lic tr
ansp
ort (
e.g.
jeep
ney,
bus
, et
c)?
Yes
no
8.
If y
es in
#7,
how
man
y m
inut
es w
alk
from
yo
ur h
ouse
to th
e w
aitin
g ar
ea?
1-5
min
s
grea
ter t
han
15 m
ins
5-
10 m
ins
Oth
ers,
spec
ify__
___
10-1
5 m
ins
9. W
hat i
s the
usu
al ti
me-
in in
wor
k pl
ace?
7-
7:30
am
7:30
-8am
8-
8:30
am
8:30
-9am
9-
9:30
am
onw
ards
Fl
exib
le w
orki
ng ti
me
O
ther
s, sp
ecify
____
____
__
H
irap
ng
mar
amin
g sy
ota
kela
ngan
da
min
g ce
llpoh
ones
5.
Wha
t is t
he se
cond
ary
mod
e* of t
rans
port
you
use
whe
n yo
u go
to sc
hool
or w
ork?
*s
econ
dary
mod
e is
an
alte
rnat
ive
mod
e, m
eani
ng if
prim
ary
is n
ot a
vaila
ble
then
dec
ide
on o
ther
tra
nspo
rt m
ode
to re
ach
to y
our d
estin
atio
n.
# Pw
ede
ang
mar
amin
g ch
eck
sa m
ode.
I-c
heck
din
kun
g pe
r day
, per
wee
k o
per m
onth
mo
ba it
ong
sina
saky
an. I
sula
t din
kun
g ila
ng b
eses
(bal
ikan
) ito
gin
agam
it pa
g pe
r day
, per
wee
k or
per
mon
th.
P
er d
ay
per
wee
k
per m
onth
H
alim
baw
a:
Jeep
ney
_
___4
___
Mea
ning
, kun
g di
ava
ilabl
e an
g pr
imar
y m
ode
so se
cond
ary
mod
e an
g ga
gam
itin
whi
ch is
jeep
ney,
at
apat
na
bese
s mo
gina
gam
it pe
r wee
k.
Che
ck th
e m
ode
(M
ultip
le a
nsw
ers O
K)
Che
ck in
the
box
the
pref
erre
d us
e of
tra
nspo
rt Pe
r day
p
er w
eek
p
er m
onth
Writ
e th
e fr
eque
ncy
of u
se o
n th
e sp
ace
prov
ided
Jeep
ney
Tric
ycle
/ ped
icab
FX
Ta
xi
M
RT tr
ain
Pr
ivat
e ca
r
Re
gula
r bus
Ai
r con
bus
N
one,
by
wal
king
O
ther
spec
ify
Ma-
text
nga
mga
fri
ends
hips
ko
para
mag
shop
ping
kam
i o k
aya
punt
a ka
mi s
a m
ay is
awan
o k
aya
punt
a sa
Q
uiap
o bi
li ng
mga
dvd
s…
221
5. P
erce
ptio
ns/A
ttitu
des
(Ple
ase
chec
k (�
) the
em
ote
icon
whi
ch y
ou th
ink
is th
e m
ost a
ppro
pria
te.)
Stro
ngly
agr
ee
Agre
e N
eutr
alD
isag
ree
Stro
ngly
Dis
agre
e N
o id
ea
1.
To w
hat e
xten
t do
you
agre
e th
at th
e us
e of
cel
l pho
ne e
ncou
rage
s you
to tr
avel
?
2.
To
wha
t ext
ent d
o yo
u ag
ree
that
the
use
of c
ell p
hone
enc
oura
ges y
ou to
mak
e m
ore
frie
nds?
3. T
o w
hat e
xten
t do
you
agre
e th
at th
e us
e of
cel
l pho
ne m
akes
you
feel
safe
and
secu
re?
4.
To
wha
t ext
ent d
o yo
u ag
ree
that
send
ing
text
mes
sage
s thr
ough
cel
l pho
nes i
s mor
e pr
actic
al
than
voi
ce c
allin
g?
5. D
o yo
u se
nd m
essa
ges t
hrou
gh m
obile
pho
ne b
ecau
se y
ou ju
st w
ant s
omeo
ne to
talk
to?
6.
Do
you
cons
ider
that
hav
ing
an m
obile
pho
ne is
Im
port
ant
C
omfo
rtab
le
Re
liabl
e
Che
ap
C
onve
nien
t
St
ylis
h an
d Fa
shio
nabl
e
Ef
fect
ive
com
mun
icat
ion
7.
How
do
you
feel
whe
n se
ndin
g te
xt m
essa
ges t
o yo
ur fr
iend
s?
Hap
py
In
spir
ed
Ex
cite
d
Irri
tate
d
Inte
rest
ed
8.
How
do
you
usua
lly fe
el w
hen
you
rece
ive
text
mes
sage
s?
Hap
py
In
spir
ed
Ex
cite
d
Irri
tate
d
In
tere
sted
“Yes
Jay
son,
yo
u m
ay g
o to
th
e la
vato
ry,
but
next
tim
e ju
st r
aise
you
r ha
nd.”
M
om! T
his
is
emba
rras
sing
!
222
1. T
ype
of “
inte
ract
ions
” 2.
For
who
m u
sual
ly
the
inte
ract
ion
is fo
r?
3. W
rite
the
lette
r for
th
e av
erag
e fr
eque
ncie
s of
the
inte
ract
ion
in
each
gro
up o
f peo
ple:
a.
� 5
tim
es a
day
b. 1
-4 t
imes
a d
ay
c.
� 5
tim
es a
wee
k d.
1-4
tim
es a
wee
k e.
1-3
times
a m
onth
f.
neve
r
4. W
rite
the
lette
r for
the
num
ber o
f pe
rson
s you
co
ntac
ted:
a.
0-4
b. 5
-9
c.
10-
14
d. 1
5-19
e.
�20
5. T
he e
stim
ated
ave
rage
du
ratio
n of
the
call
or th
e le
ngth
of
the
emai
l or l
ette
r:
1. 1
0 w
ords
6.<
30m
ins
2.
30
wor
ds
7
. 30m
ins –
1hr
3. 5
0 w
ords
8. 1
- 3ho
urs
4.
100
wor
ds
9.
>3
hour
s
5. >
150
wor
ds
6. M
otiv
atio
ns o
f doi
ng in
tera
ctio
ns
*The
m/th
ey =
mea
ns fa
mily
& fr
iend
s#W
e/us
=mea
ns I,
fam
ily &
frie
nds
Exam
ple:
for s
end
text
mes
sage
s, yo
u us
ually
text
firs
t you
r frie
nds a
nd so
yo
u ha
ve to
che
ck “
I tex
t the
m fi
rst”
C
heck
one
(1) b
ox o
nly.
7. R
ank
the
type
of
inte
ract
ion
whi
ch y
ou u
se
as m
edia
in d
iscu
ssin
g im
porta
nt m
atte
rs.
(1 b
eing
th
e m
ost m
edia
use
d an
d 7
bein
g th
e le
ast m
edia
use
d):
[I-r
ank
gina
gam
it na
in
tera
ksyo
n. 1
ang
pi
naka
mad
alas
at 7
ang
di
(1) S
end
text
mes
sage
s
1.Fa
mily
mem
bers
I tex
t the
m*
first
I
usua
lly te
xt th
em
We
text
s equ
ally
*T
hey
text
me
Th
ey te
xt m
e fir
st
2.
clo
se fr
iend
s
3. c
olle
ague
s
4. n
ot so
clo
se fr
iend
s
5. e
xten
ded
frie
nds
(2) S
end
emai
l
1.Fa
mily
mem
bers
I em
ail t
hem
firs
t I
usua
lly e
mai
l the
m
We
send
em
ails
fai
rly
They
usu
ally
em
ail m
e Th
ey e
mai
l me
first
2.
clo
se fr
iend
s
3. c
olle
ague
s
4. n
ot so
clo
se fr
iend
s
5. e
xten
ded
frie
nds
(3) S
end
inst
ant m
essa
ge
exam
ple:
yah
oo
mes
seng
er (Y
M),
MSN
, IC
Q
1.Fa
mily
mem
bers
I cha
t with
them
firs
t I
usua
lly c
hat w
ith th
em
We
chat
fai
rly
They
usu
ally
cha
t with
me
They
cha
t w
ith m
e fir
st
2.
clo
se fr
iend
s
3. c
olle
ague
s
4. n
ot so
clo
se fr
iend
s
5. e
xten
ded
frie
nds
(4) C
onta
ct b
y in
vita
tion
1.Fa
mily
mem
bers
I sen
d th
em in
vite
s fir
st
They
usu
ally
send
me
invi
tes
We
send
invi
tes f
airl
y So
meo
ne e
lse
invi
tes u
s let
ter
We
get t
o se
e ea
ch o
ther
regu
larl
y in
occ
asio
ns
2.
clo
se fr
iend
s
3. c
olle
ague
s
4. n
ot so
clo
se fr
iend
s
5. e
xten
ded
frie
nds
(5) T
alk
by c
ell p
hone
1.Fa
mily
mem
bers
I cal
l fir
st b
y ce
ll ph
one
I us
ually
cal
l by
cell
phon
e W
e ca
ll ju
stly
by
cell
phon
e Th
ey c
all m
e by
cel
l pho
ne
They
cal
l me
first
by
cell
phon
e
2.
clo
se fr
iend
s
3. c
olle
ague
s
4. n
ot so
clo
se fr
iend
s
5. e
xten
ded
frie
nds
(6) T
alk
by la
ndlin
e ph
one
1.Fa
mily
mem
bers
I cal
l fir
st b
y la
ndlin
e
I usu
ally
cal
l by
land
line
W
e ca
ll fa
irly
by
land
line
Th
ey c
all m
e by
land
line
Th
ey c
all m
e fir
st b
y la
ndlin
e
2.
clo
se fr
iend
s
3. c
olle
ague
s
4. n
ot so
clo
se fr
iend
s
5. e
xten
ded
frie
nds
(7) T
alk
in p
erso
n
1.Fa
mily
mem
bers
I inv
ite th
em fi
rst f
or ta
lk
I us
ually
invi
te th
em fo
r tal
k W
e e
qual
ly
They
invi
te m
e fo
r tal
k Th
ey in
vite
me
first
for t
alk
2.
clo
se fr
iend
s
3. c
olle
ague
s
4. n
ot so
clo
se fr
iend
s
5. e
xten
ded
frie
nds
223
In
this
sect
ion,
col
umn
1 is
the
type
s of s
ocia
l act
iviti
es. C
olum
n 2
is a
skin
g ho
w m
any
times
the
soci
al a
ctiv
ity is
per
form
ed.
Col
umn
3 as
ks fo
r wha
t typ
e of
veh
icle
use
d in
doi
ng th
e so
cial
act
ivity
. Col
umn
4 as
ks fo
r how
man
y pe
rson
s doi
ng th
e so
cial
act
ivity
. Col
umn
5 as
ks fo
r how
the
soci
al w
as p
lann
ed. C
olum
n 6
is a
skin
g th
e co
mpa
nion
s of d
oing
the
soci
al a
ctiv
ity. C
olum
n 7
is a
skin
g w
hat t
he so
cial
ac
tivity
is fo
r. C
olum
n 8
asks
for t
he u
sual
tim
e th
e so
cial
act
ivity
is d
one.
Col
umn
9 as
ks fo
r the
usu
al p
lace
the
soci
al a
ctiv
ity is
don
e.
Wri
te y
our a
nsw
er (t
he n
umbe
r) th
at c
orre
spon
ds to
the
ques
tion
in e
ach
colu
mn.
Onl
y O
NE
(1) a
nswe
r for
eac
h co
lum
n. (N
o m
ultip
le a
nsw
er.)
(Isu
lat a
ng sa
got [
ang
num
ero]
na
naay
on sa
baw
at ta
nong
. Is
ang
sago
t lam
ang
baw
at ta
nong
. )
2. T
ype
of
“Soc
ial A
ctiv
ities
”
Writ
e th
e nu
mbe
r on
the
first
col
umn
for
the
freq
uenc
y of
pe
rfor
min
g th
e so
cial
act
iviti
es?
Writ
e th
e nu
mbe
r on
the
seco
nd
colu
mn
of w
hat
type
of v
ehic
le y
ou
usua
lly u
sed
in
doin
g su
ch so
cial
ac
tiviti
es?
Writ
e th
e nu
mbe
r on
the
third
co
lum
n of
ho
w m
any
num
ber o
f fr
iend
s you
us
ually
do
the
soci
al
activ
ity?
Writ
e th
e nu
mbe
r on
the
four
th c
olum
n of
how
is th
e so
cial
act
ivity
be
ing
plan
ned?
[Isu
lat a
ng
num
ber k
ung
gaan
o ka
bilis
pi
napl
ano
ang
soci
al a
ctiv
ity?]
Writ
e th
e nu
mbe
r on
the
fifth
col
umn
of
With
who
m is
the
activ
ity u
sual
ly
perf
orm
ed?
(sin
o an
g ka
sam
a)
rela
tions
hip
Writ
e e
the
num
ber
on th
e co
lum
n of
to
who
m is
the
activ
ity
usua
lly p
erfo
rmed
?
(par
a ka
nino
ang
so
cial
ativ
ity)
Writ
e th
e nu
mbe
r co
rres
pond
ing
to
wha
t tim
e do
you
us
ually
do
the
soci
al
activ
ities
? Is
ulat
ang
tim
e ku
ng
ang
soci
al a
ctiv
ity a
y gi
naga
wa
sa w
eekd
ay
o w
eeke
nd.
Writ
e th
e nu
mbe
r on
the
colu
mn
of w
hat
spec
ific
loca
tion
do
you
usua
lly p
erfo
rm
soci
al a
ctiv
ities
?
1.
1-3
times
a d
ay
2
. 1-3
tim
es a
wee
k 3.
1-3
tim
es a
mon
th
4. N
ever
1.
priv
ate
car
2.
jeep
ney
3. tr
icyc
le/p
edic
ab
4. Ta
xi
5.
FX
6. A
irco
nd b
us
7. N
on-a
irco
nd b
us
8. M
RT tr
ain
9.
Non
e, B
y w
alk
1. a
lone
2.
tw
o 3.
gro
up o
f 34.
gro
up o
f 45.
gro
up o
f 5
6. �
6
1. a
t the
inst
ant
2. 3
0 m
inut
es
3. 1
hou
r 4.
hou
rs
5. a
day
6.
a w
eek
7. a
mon
th
8. a
yea
r ahe
ad
1. f
amily
mem
ber
2 fr
iend
s 3.
offi
cem
ates
4.
org
aniz
atio
n m
ates
5. n
eigh
bors
6.
Alo
ne
1. f
amily
mem
ber
2. f
rien
ds
3. o
ffice
mat
es
4.or
gani
zatio
n m
ates
5. n
eigh
bors
6.
Sel
f
1. 8
-12a
m
2.12
nn-4
pm
3.4p
m-8
pm
4. 8
pm u
p
1. s
hopp
ing
mal
l 2.
the
ater
s/
gym
nasi
um
3. a
mus
emen
t cen
ter
4. i
nter
net
cafe
5.
cof
fee
shop
6.
pro
vinc
e 7.
at h
ome
9. o
ffice
/wor
kpla
ce
10.
rest
aura
nts/
dine
r
wee
kday
wee
kend
(1) S
hopp
ing/
gro
cery
(2) D
inne
r/pic
nic
with
fa
mily
(3) D
inne
r/pic
nic
with
fr
iend
s
(4) V
isit
pare
nts’
/ re
lativ
es/fr
iend
s pl
ace
(5) W
atch
mov
ies o
r co
ncer
t
(6) P
layi
ng sp
orts
or
phys
ical
fitn
ess
(7) A
ttend
cel
ebra
tions
(f
iest
a, b
irthd
ay, e
tc.)
(8) O
ut-o
f-to
wn
vaca
tion
with
fam
ily/fr
iend
s
(9) O
rgan
izat
ion
m
eetin
gs/ c
hurc
h m
eetin
gs
othe
rs
224
We
hope
to c
ontin
ue th
is ty
pe o
f sur
vey
next
tim
e.
If in
tere
sted
, ple
ase
writ
e yo
ur e
mai
l add
ress
:
We
wis
h to
mee
t and
con
tact
you
aga
in in
our
futu
re re
sear
ches
.
AR
IGA
TO
U G
OZ
AIM
ASH
ITA
!!!
M
AR
AM
ING
SA
LA
MA
T!!
!
TH
AN
K Y
OU
VE
RY
MU
CH
!!!
225