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How can redesigning the local transportation system reduce automobile dependence at OSA?
An International Baccalaureate Extended Essay in Design Technology
Candidate Name: Keita Hill
Candidate Number:
Supervisor Name: Mr. Ho / Mr. Karas
Session: May 2010
Word Count: 3981
School: Old Scona Academic High School
Abstract
This paper investigates the research question, “How can redesigning the local
transportation system reduce automobile dependence at Old Scona Academic High School
(OSA)?” The approach is a case study into the factors that cause automobile dependence within
the scope of the local youth demographic attending Old Scona Academic Senior High School in
Edmonton, Alberta, Canada.
This paper investigates the research question, “How can redesigning the local
transportation system reduce automobile dependence at Old Scona Academic High School
(OSA)?” The approach is a case study into the factors that cause automobile dependence within
the scope of the local youth demographic attending Old Scona Academic Senior High School in
Edmonton, Alberta, Canada.
An observational survey administered to 271 OSA students during June 2009 provides
the data for this study. The sample represents approximately 80% of the school population, with
representation from all grade levels.
An observational survey administered to 271 OSA students during June 2009 provides
the data for this study. The sample represents approximately 80% of the school population, with
representation from all grade levels.
After analyzing the results and determining a model for transportation decision making at
OSA, this paper concludes that any transportation system in which users feel the necessity to
own a car will consequently have a high level of automobile dependence. To avoid this problem,
many improvements are needed, primarily centered on improving the convenience, timeliness,
and convenience of sustainable modes of transport. Finally, this study concludes that redesign of
the transportation network must be accompanied by education of the public into the benefits of
the new system, to make currently automobile dependent commuters aware of their alternatives.
After analyzing the results and determining a model for transportation decision making at
OSA, this paper concludes that any transportation system in which users feel the necessity to
own a car will consequently have a high level of automobile dependence. To avoid this problem,
many improvements are needed, primarily centered on improving the convenience, timeliness,
and convenience of sustainable modes of transport. Finally, this study concludes that redesign of
the transportation network must be accompanied by education of the public into the benefits of
the new system, to make currently automobile dependent commuters aware of their alternatives.
184 Words 184 Words
Table of Contents Introduction........................................................................................................................ Page 10 Introduction........................................................................................................................ Page 10 Procedure............................................................................................................................ Page 30 Procedure............................................................................................................................ Page 30 Design...................................................................................................................... Page 30 Design...................................................................................................................... Page 30 Administration ....................................................................................................... Page 40 Administration ....................................................................................................... Page 40 Analysis ............................................................................................................................... Page 50 Analysis ............................................................................................................................... Page 50 Modal Choice.......................................................................................................... Page 50 Modal Choice.......................................................................................................... Page 50 Time of Travel ........................................................................................................ Page 11 Time of Travel ........................................................................................................ Page 11 A Transportation Decision-Making Model ......................................................... Page 13 A Transportation Decision-Making Model ......................................................... Page 13 Recommendations .................................................................................................. Page 19 Recommendations .................................................................................................. Page 19 Evaluation........................................................................................................................... Page 28 Evaluation........................................................................................................................... Page 28 Conclusion .......................................................................................................................... Page 29 Conclusion .......................................................................................................................... Page 29 List of References............................................................................................................... Page 31 List of References............................................................................................................... Page 31 Appendices.......................................................................................................................... Page 32 Appendices.......................................................................................................................... Page 32 Appendix I. Annotated Survey ............................................................................. Page 32 Appendix I. Annotated Survey ............................................................................. Page 32 Appendix II. Data Tables from the Survey ......................................................... Page 37 Appendix II. Data Tables from the Survey ......................................................... Page 37
1
Introduction
While the twentieth century saw the rise of the gasoline-powered passenger car, the
twenty-first century will inevitably see its gradual replacement with sustainable modes of
transportation. These modes – notably public transit, walking and cycling – promise to reduce
the pressing environmental and social problems associated with automobile dependence. They
move people in less space, encourage physical activity, and reduce the need for carbon-based
energy, making them attractive choices for urban transportation design. An increasing number of
cities with traffic problems are turning away from costly road expansion and instead attempting a
“modal shift” to sustainable transportation.
Unfortunately, this modal shift is not easy. Automobile dependence is “set firmly in place
by low density suburbia and a car-oriented culture” (Newman and Kenworthy, 1991). Since air
pollution and infrastructure maintenance costs are negative externalities not paid for by the
driver, little economic incentive exists to change. Furthermore, people who already own and pay
for upkeep of a car are the least likely to switch to sustainable modes of transportation.
Any redesign of transportation systems must therefore consider youth, since they have
not yet invested in a car and so have the greatest economic incentive to adopt a car-free lifestyle.
If public transportation, cycling, and walking were appealing to youth, and continued to be
appealing at later ages, a gradual generational decline in car use would result. Over time, this
process would allow automobile dependent cities to become more sustainable.
Edmonton, located in central Alberta, Canada, is the most automobile-dependent major
urban region in Canada (Iveson, 2008). With a municipal population of 782,439 in 2009,
Edmonton’s transportation system was ranked (Coyne, 2009) as 27th out of 31 major Canadian
2
cities on criteria of effectiveness, cost, and
efficiency. Despite these major problems,
Edmonton's municipal government has
recognized the necessity of a modal shift
to sustainable transport. The question is
how to best encourage a city-wide modal
shift amongst tight budget restrictions.
To this end, this case study
investigates the research question, "How
can redesigning the local transportation
system reduce automobile dependence at
Old Scona Academic High School
(OSA)?" As a borderless school of 355 students, OSA draws students from throughout the city to
its central location, allowing for analysis of youth transportation choices throughout the city. The
results of this study provide insight into effective design of a sustainable transportation system
for the local youth demographic.
Location of Edmonton, Alberta, Canada
Map 1. The yellow star indicates the location of Edmonton within Canada (shaded green). Modified from: http://en.wikipedia.org/wiki/File:Canada_(orthographic_projection).svg
3
Procedure
Design Design
Full realization of redesigning the transportation system was not feasible, so I developed
an observational survey (Appendix I) to collect the following data: (1) demographics; (2) current
transportation trends for time of travel and (3) mode of transport; (4) factors effecting modal
choice; and (5) specific transportation-related preferences. An optional (6) open-ended written
response question was also included.
Full realization of redesigning the transportation system was not feasible, so I developed
an observational survey (Appendix I) to collect the following data: (1) demographics; (2) current
transportation trends for time of travel and (3) mode of transport; (4) factors effecting modal
choice; and (5) specific transportation-related preferences. An optional (6) open-ended written
response question was also included.
Each group of questions was designed to address a different aspect of the design cycle.
The table below outlines what aspects I considered in writing each set of questions.
Each group of questions was designed to address a different aspect of the design cycle.
The table below outlines what aspects I considered in writing each set of questions.
Stage of Design Cycle Stage of Design Cycle
Approach Approach
1. Identifying or clarifying a need or opportunity
‐ Background research provides insight into the reasons why automobile dependence is undesirable. ‐ Results from (3) identify the current level of automobile dependence at OSA.
2. Analyzing, researching and specifying requirements
‐ Results from (4) identify the needs of currently automobile dependent students to change, as well as the needs of current sustainable transport users to continue using these modes. ‐ Research into city policy provides further refinement of local requirements.
3. Generating ideas and solutions
‐ Background research provided potential solutions that have been successful in other cities, or that already exist as pilot projects in Edmonton. ‐ Responses to (6) provide further solutions from survey participants.
4. Developing the chosen solution
‐ Not directly addressed
4
5. Realizing the chosen solution
‐ Not directly addressed
6. Testing and evaluating the chosen solution
‐ Responses to (5) provide student evaluation of proposed solutions. ‐ Results from (1) identify of the limitations to the findings of this survey and the associated bias of the sample.
Reference: “Diploma Programme Design technology—guide First examinations 2009”
Administration
The survey was administered to 271 OSA students during math classes in June 2009.
Students were asked to consider their year-long commute, so that results would be representative
of trips made over the entire school year. Since students use computers regularly, the online
medium did not present any accessibility problems. Students who were not present or who were
not registered in math were not included in the sample (approximately 80 students).
5
Analysis
Modal Choice
Modal choice data was collected through a question series asking how often the
respondent used each mode of transportation to commute to OSA. Since the response choices
were “never”, “rarely”, “half the time”, “often”, and “always”, I first needed to develop a method
to convert these descriptive responses to quantitative data.
The model I developed is based on the simplest case possible: a student who uses only
one mode of transportation to commute to OSA. A single student commuting to OSA for one
year, always by one mode of transportation, makes one “student-year-trip” in that mode. The
assumption of this standard of comparison is that all students commute to the same number of
instructional days. With this assumption, two students who commute to school by the same mode
“half the time” would together make one student-year-trip in that mode.
Mathematically, this model is expressed by multiplying the total number of each type of
response by a conversion factor relative to 1.00 for “always”. For example, “half the time” is
approximated as 0.50, since a student making half of her commutes over the school year by a
given mode will make approximately 0.5 student-year-trips in that mode. From this model, I
developed the following equation:
6
R = (N“never”)*(0.00) +(N“rarely”)*(0.25) +(N“half the time”)*(0.50) +(N“often”)*(0.75) +(N“always”)*(1.00) Equation 1. Where R = student‐year‐trips and N = number of responses. The conversion factors used are: 0.00 for “never”, 0.25 for “rarely”, 0.50 for “half the time”, 0.75 for “often”, and 1.00 for “always”.
I considered the terms “never”, “half the time” and “always” to be absolute, with “rarely”
and “often” as the sources for error. For example, the conversion factor for “rarely” could be any
value between “never” at 0.00 and “half the time” at 0.50. To account for this uncertainty, I
developed the following formula:
Here is a sample R calculation, with uncertainty, for car passenger:
R“car” = (23)*(0.00) +(61)*(0.25) +(56)*(0.50) +(57)*(0.75) +(75)*(1.00) R“car” = 161.00 ± 28.32 student-year-trips
2 R = (61)*(0.49)+(57)*(0.99) -(61)*(0.01)-(57)*(0.51)
R = 28.32
2 R = (N“rarely”)*(0.49)+(N“often”)*(0.99) -(N“rarely”)*(0.01)-(N“often”)*(0.51) Equation 2. Generally, uncertainty can be calculated from (maximum‐minimum)/2. In this case, the maximum possible value for a given R occurs when all survey participants interpreted “rarely” as a value just under 0.50 (half the time) and “often” as a value just under 1.00 (always). Conversely, the minimum possible value occurs at the corresponding minimum interpretations. In this equation, 0.49 and 0.99 are used as the maximum conversion factors, and 0.01 and 0.51 are used as the minimum conversion factors.
7
Calculating R for each mode using the same method yields the following data:
Modal Share of Trips to OSA – Student‐year‐trips and % of Total Trips, by mode
Mode of Transport Student‐year‐trips (R) % of Total Bike 14.75 ± 3.12 4.6 ± 1.7 % Car Passenger 161.00 ± 28.32 46.8 ± 9.8 % Car Driver 20.25 ± 4.56 6.2 ± 2.3 % Public Transit 131.25 ± 25.20 38.4 ± 9.3 % Taxi 6.50 ± 3.84 2.1 ± 1.5 % Walking 12.50 ± 3.36 8.9 ± 3.0 % TOTAL 346.25 ± 68.4 107 ± 27.6 %
Finally, comparing the 2009 OSA results to the City of Edmonton High School average
from 2005 provides the following graph:
Percentage of Trips by Mode ‐ OSA compared to the Edmonton High School Average
0
10
20
30
40
50
60
Bike CarPassenger
Car Driver PublicTransit
Taxi Walking School Bus
OSA
Percen
tage of T
rips (%
)
Edmonton High Schools
* indicates that no data was available.
*
Mode of Transport
Edmonton High Schools Data Source: “2005 Household Travel Survey”, Published May 2006 by the City of Edmonton Transportation Department (Note: In this survey, “bicycle” was used instead of “bike”, “transit” was used instead of “public transit”, and “walk” was used instead of “walking”.)
8
When the data from the City of Edmonton is compared to the OSA survey results, the
same general trends are evident. In both surveys, public transit and car passenger comprise the
majority of trips, with a relatively small use of active modes (walking and bicycling).
Notable differences include OSA’s somewhat higher level of automobile dependence
compared to the Edmonton average, and proportionately lower use of public transit. OSA also
has a slightly higher use of the active modes, especially bicycling.
Since detailed survey data is not available for other Edmonton high schools, only
speculations can be made as to the causes for these discrepancies. However, OSA’s open border
policy on enrolment is likely related to the high level of automobile dependence. Unlike many
Edmonton high schools, which draw students from their local areas, most OSA students
commute longer distances. When the city is split into 7 geographic zones (see map 2, following
page), each with roughly the same total population (except for “Outside Edmonton”), the OSA
population is distributed as follows:
OSA Population, by region
9
Map 2. The 7 geographic zones used for analysis. I modified this map from “City of Edmonton Wards” map produced for the City of Edmonton Planning and Development Branch by The Cartographic Group.
Only 6% of OSA students commute to the school from the central area. The remaining
94% of trips are over 2.5 km in length, a distance too long for convenient walking, especially
when carrying heavy loads of school materials. When the modal choice data is split by
geographic zone, the correlation is obvious: the percentage of trips made by walking in the
central area is more than double the average. (See graph, following page.)
10
0
10
20
30
40
50
60
70
West North-West North-East Central South-West
South-East OutsideEdmonton
Percentage of Trips by Mode of Transport at OSA, by region Pe
rcen
tage of T
rips (%
)
Region
BikeCar PassengerCar DriverPublic TransitTaxiWalking
Limiting enrolment to the local area is not feasible because this would defeat the purpose
a designated academic high school. Instead, this data confirms the benefit of densifying the urban
form of the city, thereby reducing trip distances. The strong correlation between the short trip
distances in the central region and higher use of sustainable modes suggests that a more compact
city would have lower automobile dependency. A broad vision to increase population density has
already been recognized by the city government, so the correlation found in this case study
confirms the validity of this strategic goal within the youth user group at OSA.
Across the geographic regions, the modal choice data reveals public transit as the most
accepted sustainable mode, with a much lower uptake of active modes. One student from South-
West Edmonton expressed that “it would take years to walk/bike to school,” reflecting the
11
perception of many that distances are too long to commute by active transport. While this
perception is true for walking, many of the medium-length trips in the 3 to 8 kilometer range
could be covered by bicycle in less than 30 minutes. Thus, changing user perceptions about
cycling could substantially increase its use, a possibility further explored under the section titled
“Recommendations”.
Time of Travel The survey confirms that OSA has two rush hour periods: 7:00 a.m. to 8:00 a.m., and
3:00 p.m. to 4:00 p.m. In both cases, generated traffic peaks sharply, but occurs before the peak
of city rush hour.
Because the majority of traffic is generated within these short time frames, localized
traffic congestion becomes an inconvenience as well as a safety problem. As one survey
respondent commented, there are “so many people” waiting for buses at the end of the school
day that boarding becomes inefficient “because the driver has to see every individual pass, or
give transfers, etc.” As will be discussed later, time taken for travel is major factor when OSA
students make transportation decisions, so delays in bus service would likely deter its use.
However, apart from localized congestion, the survey results suggest that traffic
conditions during OSA students’ commutes are generally good. Compared to the hourly city
traffic volumes, OSA students are traveling during the off-peak to beginning-of-peak hours,
when traffic congestion is less. As well, since infrastructure such as bus lanes, separated LRT
right-of-ways, and express service is most helpful during the rush hour, most OSA students are
commuting during periods when these time-saving mechanisms on public transit make less of a
difference. One student from South-West Edmonton commented that “the time to take public
12
transit is about 5-6 times longer than [traveling] by car,” revealing the substantial time incentive
associated with driving. The contrast of an inefficient transit network compared to an effective
road network during the hours of travel reduces the incentive to take transit.
Percentage of Trips by Hour of Day – OSA compared to the Edmonton Average
0
5
10
15
20
25
30
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Percen
tage of T
rips (%
)
Hour of Day – Lower Bound
Edmonton (River Screenline) Data Source: “2007 Traffic Flow Map”, Published May 2008 by the City of Edmonton Transportation Department
13
A Transportation DecisionMaking Model
The data about current transportation trends confirms that OSA has a high level of
automobile dependence, and that this dependence is related to the long distances to the school –
both perceived and real. To make any change to the present circumstances requires a thorough
understanding of the transportation decision-making model used by OSA students.
To develop such a model, I designed a series of survey questions of the form, “In
deciding how to commute to OSA, rank the importance you (personally) assign to…”, followed
by a list of factors. Respondents were asked to provide a ranking on a scale of 1 to 5 for each
factor, with 5 being the most important. The responses were more or less normally distributed in
each case, showing a substantial degree of consensus on the relative importance of each of the
factors. The data shows three main types of distributions: those factors with the statistical mode
at 1 or 2, those with the statistical mode at 4 or 5, and those with a statistical median of 3. I
interpreted the three classes of distributions as illustrated below:
Type 1: Mode at 1 or 2 Type 3: Median at 3
“Not Important” “Very Important” “Somewhat Important”
Type 2: Mode at 4 or 5
14
The following table shows the relative importance of the factors from the survey,
classified in this manner:
“In deciding how to commute to OSA, rank the importance you (personally) assign to…”
Not Important Somewhat Important Very Important ‐ Social Acceptability (being cool) ‐ Enjoyment of the travel (scenery, exhilaration, etc.)
‐ Privacy ‐ Freedom while travelling (to eat, drink, talk, use portable technology, listen to music, etc.) ‐ Environmental Impacts
‐ Time Taken ‐ Reliability ‐ Safety ‐ Cost ‐ Comfort
While most of these findings are not surprising, the placement of social acceptability as a
factor of low importance clashes with prior research by Nordahl (2008) in which he found that
“public transit is often scorned and considered ‘un-cool’ by teenagers.” At least in this local
survey (of math students at an academic school), utility far outweighs social acceptability in
transportation decisions.
To ensure that student responses actually reflect how transportation decisions are made, I
also included the question, “Who decides how you commute to OSA?” Fully 81% of students
responded that they made the decision (either with their parents or on their own), confirming that
student’s opinions have a strong bearing on the actual decision-making process.
Perhaps more than any other factor, a person’s familiarity and comfort with a mode affect
transportation choices. To discover what impact this might have on the local transportation
decisions, I included the question, “Which of the following modes of transportation are you most
15
accustomed to?” The graph below shows the results, when converted to percent and compared to
the actual modal share of each mode.
% Modal Share and % Confidence in using mode at OSA, by mode of transportation
Not surprisingly, there is a very strong correlation between the comfort level with each
mode and the percentage of students using it to commute to school. The relationship is probably
causal and circular; students are most likely to use modes of transport they are comfortable with,
and in so doing become increasingly confident in using those modes of transportation. Notably,
biking and walking have higher percentage confidence of use than the actual modal share, while
car passenger and public transit have lower percentage confidence of use than modal share. This
Percen
tage (%
)
Mode of Transport
0
10
20
30
40
50
60
Bike Car Passenger Car Driver Public Transit Taxi Walking
Note: Respondents were able to select more than one response for the mode that they were most comfortable using.
16
demonstrates there are more students who would be willing and comfortable to walk or bike to
school, but whose needs are not being met by the local transportation network. These students
are instead forced to use the other modes, such as car passenger and public transit. This
observation also related to the general perception that trip distances to OSA are too long for
walking or cycling. Evidently, there are students who are comfortable using these modes when
commuting to other—probably closer—destinations, but who are not able to use these modes to
commute to OSA. This suggests a lack of good cycling and walking facilities connecting their
neighborhoods to OSA.
Even if the sustainable transportation system was the meet the needs of each user group
perfectly, some people would delay making a change, simply because of habit. To address this
possibility, I asked the question, “In general, how willing are you to make changes to your
current transportation choices?” The results showed the following distribution:
“In general, how willing are you to make changes to your current transportation choices?”
0
20
40
60
80
100
120
140
1 – Very reluctant 2 3 4 5 – Very willing
Num
ber of Respo
nses (#
)
Response
17
This nearly normal
distribution suggests that the
transit decision making model is
much like the uptake curve of
new technology, as first modeled
by Everett Rogers in 1962. The
transition to sustainable
transportation is the gradual
conversion of segments of the
population, beginning with
“innovators” and “early adopters”. Judging by the high level of current automobile dependence
locally, Edmonton would be in the early days of making that transition. Like Roger’s technology
adoption lifecycle, the transition in Edmonton away from automobile dependence is likely to be
a slow process, with only small portions of the population making the transition at first.
Technology Adoption Lifecycle
Figure 2. A graph of Everett Rogers’ Technology Adoption Lifecycle model. Source: http://en.wikipedia.org/wiki/File:DiffusionOfInnovation.png
Supporting this view, Domencich (1970), found that people rarely change their
transportation patterns except when these changes accompany other major lifestyle changes, such
as getting married or finding a new job. This explains the changing transportation patterns of
Edmonton students as they progress through the educational system and then into the workplace.
Since each new school setting (and finally the workplace) presents a lifestyle change, it is not
surprising that we should find substantial associated changes in transportation patterns. When the
modal choice data for Edmonton is plotted over this progression of stages through the
educational system, the period in which automobile dependence rises most markedly is the
transition from schools to the workplace. This is understandable, since students who are unhappy
18
with a slow and unreliable local public transit system will likely buy cars as soon as possible. If
sustainable transit were to better accommodate the needs of high school students, it would not
only increase the number of students using sustainable modes during high school, but also
prevent a shift to automobile dependence afterwards. How to best meet these needs of students is
the focus of the next section.
Modal Share by School/Workplace in Edmonton
0
10
20
30
40
50
60
70
80
Elementary Junior High Senior High Post Secondary Work
Car Driver Car Passenger
Transit School Bus
Bicycle Walk
Data Source: “2005 Household Travel Survey”, Published May 2006 by the City of Edmonton Transportation Department
Destination
Percen
tage of T
rips (%
)
19
Recommendations
There are thousands of specific
design improvements that have been
proposed to reduce automobile
dependence in cities. These range from the
general, such as developing more dense
urban centres, to the specific, such as the
“green wave” technology shown to the
right. The challenge is determining which
design improvements will have the
greatest impact for the least cost.
This section of the study suggests
which of these design improvements are
most effective towards increasing the use
of public transit, biking and walking. As
has been shown, a transportation network
that adequately fulfills the needs of the youth demographic will, over time, reduce the level of
local automobile dependence.
“Green Wave” Infrastructure in Denmark
Source: Making Cycling Irresistible (Pucher and Buehler, 2008) Original Caption: Figure 16. Green wave for cyclists in Odense, Denmark. Bright green lights on the bollards along the path pulsate in a wave‐like forward motion guiding cyclists to the next green traffic signal at 20km/hr. If cyclists keep pace with the green wave, they get green traffic signals at all intersections. Source: Troels Andersen, City of Odense
Most relevant to the redesign of Edmonton’s transportation network are the policy
recommendations outlined in the municipal Transportation Master Plan (TMP), a document
outlining the city’s strategic goals, many of which are interrelated to the reducing automobile
dependence. Such goals include, “shifting from an auto-oriented transportation system to a
20
system offering citizens more choice of transportation modes”, “[encouraging] active
transportation,” and “diminished focus on catering to commuter traffic.” In this section, I will
evaluate the various existing policy suggestions, along with new recommendations found
through this survey.
General Recommendations
Because trip length and time taken are two leading factors leading to current automobile
dependence at OSA, approaches to reduce trip length through densification and mixed use
zoning are recommended:
• Integration between land use and transportation planning to ensure that
sustainable transit is built into neighborhoods rather than added afterward;
• Policies directed at limiting urban sprawl.
Public Transit - Specific Recommendations
Several conclusions about effective public transit design for the OSA demographic are
outlined on the following page.
21
0
50
100
150
200
250
Closer
bus/L
RT stop
s
Faster
servi
ce
Warm
er wea
ther
Cleane
r/new
er ve
hicles
Others
to tak
e tran
sit w
ith
Better
trip pl
annin
g too
ls
Impro
ved r
eliab
ility
Impro
ved s
afety
Higher
servi
ce fre
quen
cy
Fare re
ducti
ons /
Free tra
nsit
Other
“Which of the following changes would most encourage you to take public transit to OSA?”
Num
ber of Respo
nses (#
)
Note: Respondents were able to select up to three responses.
22
According to the data, providing
faster service, higher service frequency
and improved reliability are the most
expedient improvements. While fare
reductions, free transit and closer
bus/LRT stops are also suggested, they
conflict with the most requested changes
in the network. Instead of providing
reduced fares, increasing service quality will show a greater increase in ridership. Similarly,
decreasing the distance between stops should be carefully balanced with the primary goal of
reducing trip times. If funds are available, the improvement of transit service by providing
cleaner and newer vehicles, better trip planning
tools, and improving safety will also show some
benefits.
“Are you more willing to travel by city bus or LRT?”
Existing and Planned LRT
Source: Transportation Master Plan
Within the OSA demographic, LRT is
somewhat more popular than city bus service,
though nearly half of users are equally willing
to travel by both modes.
To improve the reliability of public
transit and reduce the trip time, policy
objectives such as the following are therefore
recommended:
• Continuing expansion of the
23
LRT network as outlined in the TMP;
• Maintaining the current distance between bus stops at approximately 400 metres;
• Ensuring that the cost of transit does not become a significant disincentive to use
by freezing fares at 2009 levels;
• Continuing to increase service frequency on both LRT and buses, as well as
increasing the overall number of bus routes, especially in South-West Edmonton
• Development of transit avenues
(where a minimum bus frequency
of 15 minutes is provided weekday
peak, and weekday, Saturday and
Sunday midday periods), including
along Whyte Avenue, which runs a
block south of OSA;
Source: Transportation Master Plan
Proposed Transit Avenues
• Continuing to develop and expand
on-road infrastructure for buses,
including bus lanes;
• Integrating the bus and LRT
network more effectively.
24
Bicycle - Specific Recommendations
The following graph shows the improvements suggested by the survey to increase bicycle
commuting:
Which of the following changes would most encourage you to bike to OSA?
Num
ber of Respo
nses (#
)
Note: Respondents were able to select up to three responses.
0
50
100
150
200
250
Better
lighti
ng
Shorte
r dist
ance
Warm
er wea
ther
Better
scen
ery
Others
to bik
e with
Bike M
aps /
Trip pl
annin
g too
ls
Impro
ved s
afety
More/be
tter r
oad f
acilit
ies
More/be
tter b
ike pa
rking
Having
an op
eratio
nal b
ikeOthe
r
Geographic conditions, specifically the perceptions that distances are too long and
weather too cold for biking, are identified as the main disincentives. However, given that trip
distances of 3 to 8 km are easily manageable for most people by bicycle, and that at least half of
the school year have temperatures sufficiently warm for cycling, both of these perceptions are
somewhat mistaken. Thus, while these geographic factors can not be changed, educating students
about the feasibility of cycling and how to ride safely on the road could substantially increase
25
bicycle commuting. Education would simultaneously improve the safety of cycling, a third major
recommendation, since a cyclist must take much of the responsibility for his own safety on the
road.
Having “others to bike with” is also a major incentive to cycling, so schools as well as the
municipal government should continue to support community cycling programs. At OSA,
helping students create neighborhood cycling groups could substantially increase the use of this
mode in student commutes.
Improved road facilities are another major recommendation. When asked specifically
what types of road facilities are most useful, the responses were as follows:
Road Type Percentage
Bicycle paths and sidewalks shared with pedestrians (mixed‐use paths) 45%
Bike‐only lanes alongside traffic 29%
Residential roads shared with vehicles 16%
Service roads or alleys 8%
Bike lanes shared with buses/taxis 7%
Major roads shared with vehicles 1%
Road Infrastructure Preferences for Cycling to OSA
From a municipal standpoint, this survey identifies the following recommendations to
increase the use of cycling in Edmonton:
• Assist schools to integrate cycling education, both about feasibility of cycling and
bicycle safety;
• Expand the mixed-use path network and bicycle-only lanes on major roads;
• Continue to improve bicycle parking and bicycle trip-planning tools.
26
Walking - Specific Recommendations
The following graph shows the improvements suggested by the survey to increase
pedestrian commutes:
Which of the following changes would most encourage you to walk to OSA?
Num
ber of Respo
nses (#
)
Note: Respondents were able to select up to three responses.
0
50
100
150
200
250
300
Better
lighti
ng
Shorte
r dist
ance
Warm
er wea
ther
Better
scen
ery
Others
to walk
with
Walk
ing m
aps
Impro
ved s
afety
More/be
tter w
alking
facil
ities
As with cycling, trip distance and weather are the main barriers to students walking to
school. Both of these problems can not be addressed without significant changes to the urban
form of the city. However, one solution is to better integrate walking and public transit, by
reducing the walking portion of a trip to a manageable length.
27
After geographic factors, having “others to walk with” and “improved safety” are the
main desired improvements. Since walking with others also affords improved safety, a “walking
school bus” could be used to encourage group walking.
Improved road facilities are less of a concern for walking, suggesting that adequate
facilities already exist. The road infrastructure preferences for walking were as follows:
Road Infrastructure Preferences for Walking to OSA
Road Type Percentage
Sidewalks 49%
Off‐road paths only for pedestrians 28%
Off‐road paths shared with cyclists 18%
Service roads or alleys 4%
To increase the use of walking in Edmonton, policy objectives such as the following are
recommended:
• Assist schools in developing “walking school bus” programs;
• Continue to maintain the city-wide sidewalk and multiuse trail network;
28
Evaluation
There are several limitations of the data collected in this case study, most notably,
demographic biases:
There are several limitations of the data collected in this case study, most notably,
demographic biases:
• OSA is an unusual high school because it draws students from a wide geographic
range. Results from an open-border academic school can not necessarily be
extrapolated to all high schools in Edmonton.
• OSA is an unusual high school because it draws students from a wide geographic
range. Results from an open-border academic school can not necessarily be
extrapolated to all high schools in Edmonton.
• Only math students present in their classes were surveyed. While only a small
percentage of students do not take math classes, this may have resulted in
unintended bias.
• Only math students present in their classes were surveyed. While only a small
percentage of students do not take math classes, this may have resulted in
unintended bias.
• While students commute from all parts of the city, most students at OSA live in
the South-West region of the city.
• While students commute from all parts of the city, most students at OSA live in
the South-West region of the city.
• The survey sample included more females (59%) than males (41%). • The survey sample included more females (59%) than males (41%).
Missing data was not a major problem, because all questions except written response and
demographics-related items were mandatory. However, misinterpretation of survey questions,
incorrect/mistaken responses, or repeated submissions by the same respondent could have posed
undetected problems with the data.
Missing data was not a major problem, because all questions except written response and
demographics-related items were mandatory. However, misinterpretation of survey questions,
incorrect/mistaken responses, or repeated submissions by the same respondent could have posed
undetected problems with the data.
Overall, the collected data revealed substantial trends within the OSA student
demographic. The close correlation between the data from OSA and that found in previous
studies suggests a high level of validity.
Overall, the collected data revealed substantial trends within the OSA student
demographic. The close correlation between the data from OSA and that found in previous
studies suggests a high level of validity.
29
Conclusion In answer to the research question, “How can redesigning the transportation network
reduce automobile dependence at OSA,” a variety of possible improvements have been outlined
through the course of this essay. Most importantly, so long as the transportation system is
centered on the private vehicle, users will be obligated to purchase and maintain a car in order to
have access to convenient transportation. In such a system, automobile dependence will
necessarily remain high. As one OSA automobile commuter commented, the greatest
encouragement to take public transit would be “if [she] didn’t have [her] own car.” Thus, the real
city-wide reduction in automobile dependence will only occur when students—who already use
sustainable modes more than the average—not only use sustainable modes to commute to school,
but never feel obligated to buy a car in the first place.
In order to make such a drastic change, the access to quality of the transportation offered
by sustainable modes must increase drastically. To do this means to totally redesign the urban
transportation system: to reduce trip distances by increasing population density, to increase the
number of transit routes and their frequencies of service, to reduce transit fares, to expand
infrastructure for walking and cycling, and to educate the public about bicycle use and safety.
Because of the cost to taxpayers of making these changes, public support to develop and use
sustainable transit is critical. Education must be at the core of redesigning the transportation
network, so that the redesign of the system grows in sync with a public appreciation for
sustainable transit.
This survey has gone as far as to show what students need from a transportation system–
what remains is to make sure that sustainable transit offers what is being looked for, and to find
30
ways to market the new modes. It is these latter design opportunities that I leave for future
research.
31
List of References
City of Edmonton Transportation Department. (2006 May). 2005 Household Travel
Survey Overview. Edmonton, Alberta, Canada.
City of Edmonton Transportation Department. (2006 May). 2005 Household Travel
Survey Overview. Edmonton, Alberta, Canada.
City of Edmonton Transportation Department. (2009 September). The Way We Move:
Transportation Master Plan. Edmonton, Alberta, Canada.
City of Edmonton Transportation Department. (2009 September). The Way We Move:
Transportation Master Plan. Edmonton, Alberta, Canada.
Coyne, A. (2009, July 16). Canada’s best and worst run cities [Electronic version].
Maclean’s. Retrieved September 13, 2009, from http://www2.macleans.ca/2009/07/16/canadas-
best-and-worst-run-cities/
Coyne, A. (2009, July 16). Canada’s best and worst run cities [Electronic version].
Maclean’s. Retrieved September 13, 2009, from http://www2.macleans.ca/2009/07/16/canadas-
best-and-worst-run-cities/
Domencich, T. A., & Kraft G. (1970). Free Transit. Lexington, MA: Health Lexington
Books
Domencich, T. A., & Kraft G. (1970). Free Transit. Lexington, MA: Health Lexington
Books
Iveson, D. (2008, March 12). Administrative Inquiry for March 12 Council Meeting.
Administrative Inquiry made at the March 12, 2008, City Council meeting, in Edmonton, AB.
Iveson, D. (2008, March 12). Administrative Inquiry for March 12 Council Meeting.
Administrative Inquiry made at the March 12, 2008, City Council meeting, in Edmonton, AB.
Newman, P. W.G., & Kenworthy J.R. (1991). Cities and Automobile Dependence: a
sourcebook. (pp. 1-3). Aldershot Hants, England: Gower Publishing Company.
Newman, P. W.G., & Kenworthy J.R. (1991). Cities and Automobile Dependence: a
sourcebook. (pp. 1-3). Aldershot Hants, England: Gower Publishing Company.
Nordahl D. (2008). My kind of transit: rethinking public transportation in America (1st
ed.). (p. 76). Chicago: The Center for American Places at Columbia College Chicago.
Nordahl D. (2008). My kind of transit: rethinking public transportation in America (1st
ed.). (p. 76). Chicago: The Center for American Places at Columbia College Chicago.
Pucher, J. & Buehler, R. (2008). Making Cycling Irresistible: Lessons from The
Netherlands, Denmark and Germany. Transport Reviews, 28:4, 495-528.
Pucher, J. & Buehler, R. (2008). Making Cycling Irresistible: Lessons from The
Netherlands, Denmark and Germany. Transport Reviews, 28:4, 495-528.
32
Appendix I. Annotated Survey
In Alberta, driver’s licenses are issued in Classes 1 to 7. Those with Class 5 licenses can drive cars and pickup trucks. Those with Class 4 (and under) licenses can additionally drive other vehicles such as taxis, buses, and freight trucks. Class 7 is called a “Learner’s Permit” because the permit holder can drive a car or pickup truck, but under the condition that there is a Class 5 (or lower) license‐holder supervising in the vehicle.
33
At OSA, early morning classes start at 7:09 AM, and regular classes start at 8:02 AM. Doors open at 6:50 AM.
At OSA, regular classes end at 3:26 PM, although some students can leave earlier during their spare blocks. Late evening classes end at either 5:11 PM or 5:31 PM.
I designed this stem to account for student transportation patterns that vary by weather, time of day, day of week, season, etc. Here, my aim is to determine the approximate modal shares of total trips throughout the year, rather than the specific circumstances in which each mode is used.
34
35
36
[End of Appendix]
37
Appendix II. Data Tables from the Survey The tables below contain the breakdown of responses to each of the questions in the
survey (Appendix I). The raw data is provided digitally on a CD (Appendix III).
The tables below contain the breakdown of responses to each of the questions in the
survey (Appendix I). The raw data is provided digitally on a CD (Appendix III).
Notes Notes
• In questions marked with an asterisk (*), respondents were able to choose multiple
answers, leading to some totals that were greater than the sample size.
• In questions marked with an asterisk (*), respondents were able to choose multiple
answers, leading to some totals that were greater than the sample size.
• The following abbreviations are used for the geographic zones: (See page 9 for map.) • The following abbreviations are used for the geographic zones: (See page 9 for map.)
1 W 1 W 2 NW 2 NW 3 NE 3 NE 4 C 4 C 5 SW 5 SW 6 SE 6 SE 7 O 7 O Zone 1 West
Zone 2 North‐West
Zone 3 North‐East
Zone 4 Central
Zone 5 South‐West
Zone 6 South‐East
Zone 7 Outside Edmonton
• Selected responses from written response questions are included as the last table of this
appendix.
• Uncertainties and units of measurement were excluded intentionally due to the non-
dimensional nature of the data.
• In Question 1.4, many respondents identified their location based on the city’s quadrant
system (Appendix 4), which was problematic because more than 90% of the city
officially lies in the north-west. To resolve this issue, I manually re-sorted the data
individually based on the 7-Zone system (Page 9). The data reflects the location of the
neighborhoods collected from the question “From what neighborhood do you commute to
OSA?” rather than the faulty responses collected in Question 1.4.
38
1 Demographics of Survey Participants at OSA Question and Answer Number of Responses (N) 1.1 Are you male or female? Overall 1 W 2 NW 3 NE 4 C 5 SW 6 SE 7 O
Male 112 22 8 5 11 52 12 2 Female 159 12 11 10 6 94 20 6 (No response) 1 0 0 0 0 1 0 0 1.2 Do you have a valid Alberta driver's ‐‐‐license?
Overall
1 W
2 NW
3 NE
4 C
5 SW
6 SE
7 O
Yes, Class 7 (learner's) 103 11 7 7 6 60 11 1 Yes, Class 5 (or under) 34 0 1 1 4 18 3 7 No. 131 23 11 7 7 65 18 0 (No response) 4 0 0 0 0 4 0 0 1.3 How old are you? Overall 1 W 2 NW 3 NE 4 C 5 SW 6 SE 7 O 14 0 0 0 0 0 0 0 0 15 58 11 3 1 3 33 7 0 16 108 10 10 9 6 54 16 3 17 76 10 5 3 7 40 8 3 18 28 3 1 1 0 20 1 2 19 1 0 0 1 0 0 0 0 (No response) 1 0 0 0 0 1 0 0 1.4 In which geographic region of ‐‐‐Edmonton is your neighborhood? ‐‐‐† (See note, above.)
Overall
Zone 1 ‐ West 34 Zone 2 ‐ North‐West 19 Zone 3 ‐ North‐East 15 Zone 4 ‐ Central 17 Zone 5 ‐ South‐West 146 Zone 6 ‐ South‐East 32 Zone 7 ‐ Outside Edmonton 8
2 Current Transportation Trends at OSA – Time of Travel Question and Answer Number of Responses (N) 2.1 What time do you leave on your ‐‐‐morning commute to OSA?*
Overall
1 W
2 NW
3 NE
4 C
5 SW
6 SE
7 O
5:30 to 6:00 AM 8 1 3 1 0 3 0 0 6:00 to 6:30 AM 30 5 7 4 0 12 2 0 6:30 to 7:00 AM 81 10 8 6 3 41 12 1 7:00 to 7:30 AM 149 14 9 6 7 86 20 7 7:30 to 8:00 AM 65 6 2 1 9 40 7 0 8:00 to 8:30 AM 10 0 1 0 1 6 2 0 Other 2 ‐ ‐ ‐ ‐ ‐ ‐ ‐ (No response) 0 0 0 0 0 0 0 0
[Continued…]
39
2.2 How long, on average, does your ‐‐‐morning commute take?*
Overall
1 W
2 NW
3 NE
4 C
5 SW
6 SE
7 O
0:00 to 0:15 23 1 1 1 7 11 2 0 0:15 to 0:30 159 14 7 2 5 102 22 7 0:30 to 0:45 84 12 8 6 3 44 9 2 0:45 to 1:00 36 8 6 5 1 15 0 1 1:00 to 1:15 18 3 3 3 0 7 2 0 1:15 to 1:30 10 5 0 2 0 1 2 0 more than 1:30 3 2 0 0 0 1 0 0 (No response) 0 0 0 0 0 0 0 0 2.3 What time do you leave on your ‐‐‐ evening commute from OSA?*
Overall
1 W
2 NW
3 NE
4 C
5 SW
6 SE
7 O
1:00 to 2:00 PM 22 2 3 2 1 12 2 0 2:00 to 3:00 PM 59 5 5 1 5 37 5 1 3:00 to 4:00 PM 218 28 17 13 13 117 23 7 4:00 to 5:00 PM 69 9 4 2 5 36 11 2 5:00 to 6:00 PM 53 3 3 4 4 34 4 1 Other 15 ‐ ‐ ‐ ‐ ‐ ‐ ‐ (No response) 0 0 0 0 0 0 0 0 2.4 How long, on average, does your ‐‐‐evening commute take?*
Overall
1 W
2 NW
3 NE
4 C
5 SW
6 SE
7 O
0:00 to 0:15 14 0 0 0 6 6 2 0 0:15 to 0:30 90 2 4 1 7 54 15 7 0:30 to 0:45 84 11 6 3 2 50 11 1 0:45 to 1:00 63 9 8 8 1 33 3 1 1:00 to 1:15 57 10 4 6 0 32 4 1 1:15 to 1:30 43 12 4 4 1 18 4 0 more than 1:30 10 5 1 0 0 2 2 0 (No response) 0 0 0 0 0 0 0 0
3 Current Transportation Trends at OSA – Modal Choice Question and Answer Number of Responses (N) 3.1 Throughout the year, how often do ‐‐‐you commute to OSA…
…by bike? Overall 1 W 2 NW 3 NE 4 C 5 SW 6 SE 7 O Never 247 32 17 13 10 136 31 8 Rarely 11 1 0 1 3 6 0 0 Half the time 3 1 0 0 1 1 0 0 Often 2 0 0 0 0 2 0 0 Always 9 0 2 1 0 5 1 0 (No response) 0 0 0 0 0 0 0 0 …by car (driven by someone ‐‐‐else)?
Overall
1 W
2 NW
3 NE
4 C
5 SW
6 SE
7 O
Never 23 5 0 2 4 8 3 1 Rarely 61 6 6 6 7 28 7 1 Half the time 56 7 7 1 0 35 3 3 Often 57 8 3 2 1 32 10 1 Always 75 8 3 4 2 47 9 2 (No response) 0 0 0 0 0 0 0 0
40
[3.1 Continued] Throughout the year, ‐‐‐how often do you commute to OSA…
…by car (on your own)? Overall 1 W 2 NW 3 NE 4 C 5 SW 6 SE 7 O Never 238 33 17 15 12 131 28 2 Rarely 13 1 1 0 0 7 2 2 Half the time 5 0 0 0 0 2 1 2 Often 6 0 0 0 2 3 0 1 Always 10 0 1 0 0 7 1 1 (No response) 0 0 0 0 0 0 0 0 ...by public transit? Overall 1 W 2 NW 3 NE 4 C 5 SW 6 SE 7 O Never 47 3 3 3 3 28 4 3 Rarely 70 7 3 3 2 46 8 1 Half the time 65 11 7 2 1 34 8 2 Often 35 2 2 2 6 19 2 2 Always 55 11 4 5 2 23 10 0 (No response) 0 0 0 0 0 0 0 0 …by taxi? Overall 1 W 2 NW 3 NE 4 C 5 SW 6 SE 7 O Never 254 33 18 14 13 139 29 8 Rarely 15 1 1 0 1 10 2 0 Half the time 0 0 0 0 0 0 0 0 Often 1 0 0 0 0 0 1 0 Always 2 0 0 1 0 1 0 0 (No response) 0 0 0 0 0 0 0 0 …by walking? Overall 1 W 2 NW 3 NE 4 C 5 SW 6 SE 7 O Never 248 31 16 13 7 142 31 8 Rarely 12 1 1 1 5 4 0 0 Half the time 4 1 0 0 0 2 1 0 Often 2 0 0 0 1 1 0 0 Always 6 1 2 1 1 1 0 0 (No response) 0 0 0 0 0 0 0 0
4 Factors Effecting Modal Choice at OSA Question and Answer Number of Responses (N) 4.1 Who decides how you commute to ‐‐‐OSA?
Overall
1 W
2 NW
3 NE
4 C
5 SW
6 SE
7 O
I decide. 35 2 1 3 4 22 1 2 My parents/guardians decide. 51 5 3 3 1 28 10 1 We both decide. 186 27 15 9 9 100 21 5 (No response) 0 0 0 0 0 0 0 0 4.2 How much have you critically ‐‐‐compared and evaluated your ‐‐‐transportation options to OSA?
Overall
1 W
2 NW
3 NE
4 C
5 SW
6 SE
7 O
1 – I have never thought about it. 26 1 3 2 1 15 3 1 2 53 7 2 1 1 35 5 2 3 83 5 7 5 4 49 11 2 4 71 11 4 3 4 39 9 1 5 – I have thoroughly thought it out. 39 10 3 4 4 12 4 2 (No response) 0 0 0 0 0 0 0 0
41
4.3 In deciding how to commute to ‐‐‐OSA, rank the importance you ‐‐‐(personally) assign to…
…comfort Overall 1 W 2 NW 3 NE 4 C 5 SW 6 SE 7 O 1 – Not important 10 3 0 1 2 3 1 0 2 33 6 3 1 2 17 3 1 3 78 8 6 4 3 49 4 4 4 101 14 5 6 5 55 16 0 5‐ Very important 50 3 5 3 2 26 8 3 (No response) 0 0 0 0 0 0 0 0 ...cost Overall 1 W 2 NW 3 NE 4 C 5 SW 6 SE 7 O 1 – Not important 27 2 1 4 0 15 4 1 2 41 8 4 0 3 18 6 2 3 53 7 3 4 2 33 4 0 4 85 9 7 2 4 47 12 4 5‐ Very important 66 8 4 5 5 37 6 1 (No response) 0 0 0 0 0 0 0 0 ...environmental impacts Overall 1 W 2 NW 3 NE 4 C 5 SW 6 SE 7 O 1 – Not important 52 10 5 5 1 25 4 2 2 63 5 3 0 2 42 9 2 3 91 10 6 6 5 47 13 4 4 45 5 3 3 3 26 5 0 5‐ Very important 21 4 2 1 3 10 1 0 (No response) 0 0 0 0 0 0 0 0 ...enjoyment of the travel ‐‐‐(scenery, exhilaration, etc.)
Overall
1 W
2 NW
3 NE
4 C
5 SW
6 SE
7 O
1 – Not important 125 14 7 6 6 72 14 6 2 68 9 6 1 3 41 8 0 3 45 8 5 3 1 21 7 0 4 23 2 1 3 2 11 2 2 5‐ Very important 11 1 0 2 2 5 1 0 (No response) 0 0 0 0 0 0 0 0 ...freedom while traveling (to ‐‐‐eat, drink, talk, use portable ‐‐‐technology, listen to music, etc.)
Overall
1 W
2 NW
3 NE
4 C
5 SW
6 SE
7 O
1 – Not important 39 3 4 3 2 21 6 0 2 68 9 6 1 3 41 8 0 3 45 8 5 3 1 21 7 0 4 23 2 1 3 2 11 2 2 5‐ Very important 57 6 6 5 3 26 8 3 (No response) 0 0 0 0 0 0 0 0 ...privacy Overall 1 W 2 NW 3 NE 4 C 5 SW 6 SE 7 O 1 – Not important 39 6 5 4 2 16 4 2 2 52 11 3 2 5 27 2 2 3 96 10 5 5 4 61 10 1 4 53 5 2 1 2 29 12 2 5‐ Very important 32 2 4 3 1 17 4 1 (No response) 0 0 0 0 0 0 0 0
42
[4.3 Continued] … ...reliability (ability to keep a ‐‐‐consistent schedule)
Overall
1 W
2 NW
3 NE
4 C
5 SW
6 SE
7 O
1 – Not important 6 1 0 2 0 3 0 0 2 8 1 1 0 1 4 1 0 3 22 3 1 3 2 7 3 3 4 67 10 4 3 4 34 11 1 5‐ Very important 169 19 13 7 7 102 17 4 (No response) 0 0 0 0 0 0 0 0 ...safety Overall 1 W 2 NW 3 NE 4 C 5 SW 6 SE 7 O 1 – Not important 8 2 1 2 0 2 1 0 2 19 4 0 1 1 10 1 2 3 33 3 2 2 4 15 6 1 4 77 8 5 4 3 44 11 2 5‐ Very important 135 17 11 6 6 79 13 3 (No response) 0 0 0 0 0 0 0 0 ...social acceptability (being cool) Overall 1 W 2 NW 3 NE 4 C 5 SW 6 SE 7 O 1 – Not important 140 20 8 10 10 76 13 3 2 61 6 7 2 4 33 5 4 3 46 3 3 1 0 28 11 0 4 15 5 1 1 0 6 1 1 5‐ Very important 10 0 0 1 0 7 2 0 (No response) 0 0 0 0 0 0 0 0 ...time taken Overall 1 W 2 NW 3 NE 4 C 5 SW 6 SE 7 O 1 – Not important 3 1 0 1 0 0 0 1 2 7 1 1 1 0 3 1 0 3 21 0 2 2 1 8 6 2 4 71 9 3 2 6 40 10 1 5‐ Very important 170 23 13 9 7 99 15 4 (No response) 0 0 0 0 0 0 0 0 4.4 Which of the following modes of ‐‐‐transportation are you most ‐‐‐accustomed to?*
Overall
1 W
2 NW
3 NE
4 C
5 SW
6 SE
7 O
Bike 42 6 2 2 3 24 3 2 Car (driven by someone else) 233 33 14 11 10 127 31 7 Car (on your own) 33 1 2 0 2 19 2 7 Public Transit 190 28 14 11 10 102 20 5 Taxi 9 0 0 0 2 5 1 1 Walking 81 12 7 6 5 42 7 2 (No response) 0 0 0 0 0 0 0 0 4.5 In general, how willing are you to ‐‐‐make changes to your current ‐‐‐transportation choices?
Overall
1 W
2 NW
3 NE
4 C
5 SW
6 SE
7 O
1 – Very reluctant 16 1 2 2 1 6 3 1 2 55 6 1 5 4 29 9 1 3 121 14 8 6 6 74 9 4 4 64 11 8 2 2 31 8 2 5 – Very willing 16 2 0 0 1 10 3 0 (No response) 0 0 0 0 0 0 0 0
43
5 Specific Transportation‐related Preferences at OSA Question and Answer Number of Responses (N) 5.1 Are you more willing to travel by ‐‐‐city bus or LRT?
Overall
1 W
2 NW
3 NE
4 C
5 SW
6 SE
7 O
City bus 56 7 4 0 2 25 15 3 LRT 81 12 5 6 5 44 6 3 Both equally 135 15 10 9 7 81 11 2 (No response) 0 0 0 0 0 0 0 0
5.2 For biking to OSA, which of the ‐‐‐following would you prefer to use?*
Overall
1 W
2 NW
3 NE
4 C
5 SW
6 SE
7 O
Bicycle paths and sidewalks shared ‐‐‐with pedestrians (mixed‐use paths)
145
16
9
7
10
81
17
5
Bike‐only lanes alongside traffic 94 12 6 8 7 49 9 3 Bike lanes shared with buses/taxis 21 3 1 1 3 13 0 0 Residential roads shared with vehicles 50 11 1 1 4 29 3 1 Major roads shared with vehicles 4 0 1 0 1 2 0 0 Service roads or alleys 25 4 1 1 4 9 6 0 I don't know. 107 18 10 5 3 56 12 3 (No response) 0 0 0 0 0 0 0 0
5.3 For walking to OSA, which of the ‐‐‐following would you prefer to use?*
Overall
1 W
2 NW
3 NE
4 C
5 SW
6 SE
7 O
Off‐road paths only for pedestrians 114 16 7 4 7 64 12 4 Off‐road paths shared with cyclists 72 9 4 4 4 38 8 5 Service roads or alleys 18 2 2 2 2 8 2 0 Sidewalks 198 27 13 12 10 109 22 5 I don't know. 63 6 7 3 3 32 9 3 (No response) 0 0 0 0 0 0 0 0
5.4 Which of the following changes ‐‐‐would most encourage you to bike to OSA?*
Overall
1 W
2 NW
3 NE
4 C
5 SW
6 SE
7 O
Better lighting 12 3 1 1 0 6 1 0 Shorter distance 229 30 16 15 6 130 25 7 Warmer weather 148 19 9 9 8 86 13 4 Better scenery 19 2 2 2 2 9 2 0 Others to bike with 99 12 6 5 3 60 12 1 Bike Maps / Trip planning tools 28 3 0 4 1 18 2 0 Improved safety 105 14 5 5 6 63 9 3 More/better road facilities 80 9 6 4 4 50 4 3 More/better bike parking 35 5 2 1 3 18 4 2 Having an operational bike 41 8 2 2 3 21 4 1 Other 30 ‐ ‐ ‐ ‐ ‐ ‐ ‐ (No response) 0 0 0 0 0 0 0 0
[Continued…]
44
5.5 Which of the following changes ‐‐‐would most encourage you to walk to OSA?
Overall
1 W
2 NW
3 NE
4 C
5 SW
6 SE
7 O
Better lighting 18 3 0 2 2 11 0 0 Shorter distance 246 32 17 14 8 141 26 8 Warmer weather 157 23 8 6 7 92 16 5 Better scenery 28 4 2 1 3 13 4 1 Others to walk with 124 12 8 8 3 80 12 1 Walking maps 22 3 0 3 1 14 1 0 Improved safety 92 11 7 8 6 47 10 3 More/better walking facilities 40 5 3 4 2 20 5 1 Other 21 ‐ ‐ ‐ ‐ ‐ ‐ ‐ (No response) 0 0 0 0 0 0 0 0
5.6 Which of the following changes ‐‐‐would most encourage you to take ‐‐‐public transit to OSA?
Overall
1 W
2 NW
3 NE
4 C
5 SW
6 SE
7 O
Closer bus/LRT stops 102 9 6 5 2 63 11 6 Faster service 219 30 17 12 12 118 25 5 Warmer weather 38 2 3 3 0 22 7 1 Cleaner/newer vehicles 51 8 2 3 4 27 6 1 Others to take transit with 74 6 5 6 3 47 5 2 Better trip planning tools 23 3 2 2 1 14 0 1 Improved reliability 111 19 5 7 6 57 13 4 Improved safety 37 8 1 4 2 18 4 0 Higher service frequency 135 22 9 7 9 73 13 2 Fare reductions / Free transit 98 14 4 6 3 60 9 2 Other 15 ‐ ‐ ‐ ‐ ‐ ‐ ‐ (No response) 0 0 0 0 0 0 0 0
6 Selected Written Responses Question Answer What time do you leave on your ‐‐‐evening commute from OSA?
‐ “11:00AM to 12:30PM” (10 similar responses)
If you use a mode of transportation ‐‐‐not mentioned above, please specify:
‐ “Motorcycle”
Which of the following changes would ‐‐‐most encourage you to bike to OSA?
‐ “Exorbitant gas prices” ‐ “Actually know how to ride a bike” ‐ “Less Contruction” ‐ “If I there is no hill on my way to school.” ‐ “BETTER DRIVERS”
Which of the following changes would ‐‐‐most encourage you to walk to OSA?
‐ “LIGHTER BACKPACK” (4 similar responses)
Which of the following changes would ‐‐‐most encourage you to take public ‐‐‐transit to OSA?
‐ “if i didn't have my own car” ‐ “it takes so long for me, especially for the final bus (only comes every 30 mins) so if i miss it, it takes forever” ‐ “The transit and LRT connect better. Right now I might end up waiting almost 30min for a bus to arrive after taking the LRT”
45
[continued…] If there are other considerations that ‐‐‐affect your transportation choices to ‐‐‐OSA, please add them below:
‐ “Having a "park‐and‐ride" so i could drive to a place where bus/public transit stops exist. that would be lovely. :)” ‐ “Most of my considerations in choosing my mode of transport are based on the fact that i live too far away to take any other mode of transport and get to school on time” ‐ “distance for sure cuz i live in riverbend and it would take years to walk/bike to school…” ‐ “I live in Sherwood Park, so biking/walking are both pretty much out of the question. There is no kind of incentive that would convince me to bike or walk to school.” ‐ “For biking, amount of hills in terrain. More hills = harder and longer commute.” ‐ “Winter is a bitch. ‐50, are you serious!? Dibs out.” ‐ “…sadly, i live in a new area where a 15 minute commute by walk is required to bus. Edmonton is too large of a city for me to walk or bike from my location. And cold... you know. Why does edmonton have to be so sparse?” ‐ “My health does not always allow me the luxury of taking the time to walk or bike the distance I live from the school. My parents do not trust me enough to take the bus. I carpool to save energy, but my varying schedule/time taken to get home is a big issue when I have such a heavy work/study load.” ‐ “I wouldn't mind taking the bus, but it takes 3X‐4X longer than getting a ride, is expensive, and is unreliable...” ‐ “I like the transportation system in Hong Kong. There are a lot more stops all over the city and the transportation is easily accessable. Since Edmonton gets pretty cold in the winter time, it would be better to have a heated underground LRT system. If there is a stop in almost every neighbourhood it will be more convenient and efficient. And safety is also a big issue for me.” ‐ “It takes about two hours to take the bus from and to OSA. It's very disappointing.” ‐ “Distance, i simply live toooooooooooooooooooooo far away to really have any choices ( I don't want to have to walk for like 4 hours )” ‐ “Since I have a morning class, it is very difficult for me to commute by walking/biking. In the winter, it is a lot colder out; therefore, walking to school would endanger my safety and my life. I live in the Northwest of the city so it takes a long time to get to school by bus. This is very annoying with the inefficient bus routes and the chroniclly late buses.”
46
[Continued] If there are other ‐‐‐considerations that affect your ‐‐‐transportation choices to OSA, please ‐‐‐add them below:
‐ “Perhaps I would like increased bus services in the mornings and evenings when there are the most riders, and ETS could get rid of a few of the buses that come during the day in residential areas and have three people on them. Perhaps you could get rid of one of those buses and add them to those random gaps in the evening or morning where buses will come, say, at 5:15, 5:30 and then not until 6:00. Those are just my thoughts.” ‐ “…As well, I'm a girl, which makes walking/biking/taking the bus alone not a very enjoyable experience,nor do I feel very safe alone, as most of the students who attend OSA do not live in the same area of town as I do.” ‐ “The main reason for my transportation choices are dependant upon the distance from home to school. Living in southern edmonton, not too far from the airport, the time to take public transit is about 5‐6 times longer than travelling by car. ” ‐ “Showing bus passes is very ineffective when bussing from OSA. There's so many people, and it takes so long because the driver has to see every individual pass, or give transfers, etc.” ‐ “The only thing is that buses don't run efficiently from my house to the school. The 94 (Super express from heritage to university) used to run until April, when University was over, and then I couldn't bus anymore because the bus schedules suck otherwise.”
[End of Appendix]
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