thesis ssd defence

8/14/2019 Thesis SSD Defence

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Planning of Rural Feeder Service to BusStop

Thesis submitted in partial fulfillment

Of the requirement for the award of the degree of

Doctor of Philosophy

in

Civil Engineering

By

Sudhanshu Sekhar Das

Department of Civil Engineering

Indian Institute of Technology, KharagpurKharagpur 721302, India

January 2008


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Indian Institute of Technology


INDIAN INSTITUTE OF TECHNOLOGY, KHARAGPUR

CERTI FI CATE

This is to certify that the thesis entitled Plann ing o f Rur a l Feeder Serv ice

to Bus Stop that is being submitted by Shr i Sudh anshu Sekhar Dasto the

Indian Institute of Technology, Kharagpur for the award of the degree of

Doctor of Philosophy is a record of bonafide research work carried out by him

under my supervision and guidance.

Shr i Sudh anshu Sekhar Dashas worked on the problem for over four yearsand the thesis, in my opinion, is worthy of consideration for the award of the

degree of Doctor o f Ph i losophy i n Eng ineer ing in accordance with theregulations of the Institute.

The results embodied in this thesis have not been submitted to any other

University or Institute for the award of any Degree or Diploma.

(Dr. Bhargab Maitra)

Associate Professor


Indian Institute of Technology, Kharagpur


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Dedicated to

The Almighty

&

My Parents


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ACKNOWLEDGEMENT

I take this opportunity to express my deep sense of gratitude to my

supervisor Dr. Bhargab Maitra for his invaluable guidance, advice and

constant encouragement through out the period of present research work. I

express my sincere thanks to Prof. B. B. Pandey, Prof. K. Sudhakar Reddy

and Dr. Amarnatha Reddy for their suggestions and help throughout the

present research work.

I wish to record my indebtedness to Prof. S. P. Das Gupta, Head of the Civil

Engineering Department for providing all facilities and extending help during

my research work. I express my sincere thanks to my doctoral scrutinycommittee members, Prof. R. N. Dutta and Dr. D. J. Sen for their valuable

suggestions during different stages of my research.

I owe my deep sense of gratitude to my friends and co-scholars Amar,

Debabrata, Debasis, Kishore, Phani, Sridhar, Tusar, Umesh for their

valuable and timely help in many aspects. Special thanks to Mr. Asim, for

his help in carrying out the surveys.

At last but not least, I express my heartfelt gratitude and regards to my

father (Dr. S. N. Das), mother (Mrs. K. Das) their love, patience, sacrifice

and constant inspiration and hearty thanks to my wife (Lasyamayee) and

son (Satyajeet) for their patience, tolerance and understanding my

difficulties and other family members, who have extended their active

cooperation by taking a lot of pain and anxiety during my Ph.D study

without which this work cant be fruitful one. I acknowledge the

appreciation, understanding, inspiration and support I received from them.

Date: (Sudhanshu Sekhar Das)


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i

ABSTRACT

In rural areas of developing countries, the private vehicle ownership isgenerally low and therefore, the travel need is largely served by public

transportation system. In India, all the major roads are generally served by

bus transportation system. But, in most of the rural areas, feeder services

are not available for providing transportation linkage between village

settlements and bus stop. In the present work, an investigation is carried

out on planning of rural feeder service to bus stop. The work is

demonstrated with reference to a case study in rural India. Two small

carriers namely Trekker and Tempo are considered as feeder vehicles.Three different forms of operation of feeder vehicles are investigated: fixed-

schedule, dial-a-ride and dial-a-slot. Dial-a-ride and dial-a-slot are designed

as demand-responsive forms of operation.

Travel behaviour analysis constitutes a significant part of the work. As a

part of travel behaviour analysis, a stated choice survey instrument is

designed with three alternative forms of operation of feeder vehicles and

two alternative vehicle types. Both quantitative and qualitative attributes

are included in stated choice experiment. The stated choice data collected

from rural trip makers are analyzed in two stages. In Stage-I, the stated

choice data is analyzed using Multinomial Logit, Nested Logit, Covariance

Heterogeneity Nested Logit and Random Parameter Logit (RPL) model

specifications, and willingness-to-pay (WTP) values are estimated for all the

attributes of hypothetical feeder service. In RPL models, all the random

parameters are assumed to follow the constrained triangular distribution

due to its advantages and attempts are made to take into account the effect

of socio-demographic of trip makers on WTP values. In Stage-II analysis,

WTP values obtained from Stage-I are used judiciously to reduce the

number of variables describing the hypothetical feeder service. Separate

utility equations are developed for bicycle and motor cycle users.


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ii

Using the utility equations and WTP values obtained from travel behaviour

analysis, operationally viable feeder routes are designed assuming fixed-

schedule form of operation of vehicles. Generalized cost and passenger-km

are the two alternative measures of effectiveness considered during the

design of feeder service. Several alternative scenarios are formulated

considering external subsidy and cross subsidy as the two policy

instruments. A heuristic approach is followed for the selection of feeder

routes and vehicle for all these scenarios. The uncertainties associated with

the use of utility equations developed from stated preference data for

demand estimation is duly considered, and the recommended routes are

classified as stable and unstable routes. Finally, a comparison is made

among three different forms of operation of feeder vehicles. For this

purpose, passenger movements along the selected routes are simulated

under different forms of operation of feeder vehicles. Altogether, a

comprehensive approach is demonstrated for the planning of rural feeder

service to bus stop with due consideration to travel demand, characteristics

of feeder service including type of vehicle and form of operation, behaviour

of trip makers, operational viability of feeder service, and role of policy

measures.


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Content

iii

Content

Title Page

Certificate

Dedication

Acknowledgement

Abstract i

Contents iii

List of Table vii

List of Figure xi

Chapter 1. Introduction

1.1 MOTIVATION 1

1.2 OBJECTIVE AND SCOPE OF THE WORK 2

1.3 FEEDER VEHICLES FORMS OF OPERATION 4

1.4 STUDY AREA 6

1.5 ORGANIZATION OF REPORT 8

1.6 SUMMARY 8

Chapter 2. Approach and Methodology 9

2.1 INTRODUCTION 9

2.2 TRAVEL BEHAVIOUR ANALYSIS 9

2.2.1 Design of Experiment 10

2.2.1.1 Type of Data and Preference Elicitation Technique 11

2.2.1.2 Attributes and Their Levels 15

2.2.1.3 Choice Sets 16

2.2.1.4 Questionnaire Design and Pilot Survey 19

2.2.2 Collection of Data and Development of Database 19

2.2.3 Analysis of Data 21

2.2.3.1 Organization of Data 21

2.2.3.2 Econometric Models 22

Multinomial logit 22

Nested logit 23

Covariance heterogeneity nested logit 24

Random parameter logit 24


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Content

iv

Title Page

Distributions of random parameters 26

Selection of points for RPL model 27

2.2.4 Valuing of Attributes 28

2.2.5 Comparison of Utility Equations 31

2.2.5.1 The Likelihood Ratio Test 32

2.2.5.2 Goodness of Fit 32

2.2.6 Selection of Models for Estimation of Ridership 33

2.2.7 Development of Generalized Cost Equations 34

2.3 DESIGN OF FEEDER SERVICE 35

2.3.1 Database 35

2.3.1.1 Travel Demand to Bus Stop 36

2.3.1.2 Temporal Variation of Demand 36

2.3.1.3 Road Network 37

Road network to bus stop 37

2.3.1.4 Cutoff Revenue 38

2.3.2 Measurement of Effectiveness, Alternative Scenarios

and Fare Level

38

2.3.3 Selection of Feeder Route and Vehicle 39

2.3.3.1 External Subsidy 40

2.3.3.2 Cross Subsidy 44

2.3.4 Forms of Operation of Feeder Vehicles 47

2.4 SUMMARY 48

Chapter 3. Travel Behaviour Analysis

3.1 INTRODUCTION 49

3.2 DESIGN OF EXPERIMENT 49

3.2.1 Attributes and Their Levels 50

3.2.2 Choice Sets 51

3.2.3 Questionnaire and Pilot Survey 52

3.3 COLLECTION OF DATA AND DEVELOPMENT OF DATABASE 54

3.4 ANALYSIS OF DATA: STAGE-I 55

3.4.1 Organization of Data 56

3.4.2 Multinomial Logit Model 57

3.4.3 Nested Logit Model 58


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Content

v

Title Page

3.4.4 Covariance Heterogeneity Nested Logit 61

3.4.5 Random Parameter Logit Model 62

3.4.6 Estimation of WTP Value 64

3.4.7 Comparison of Utility Equations 68

3.5 ANALYSIS OF DATA: STAGE-II 70

3.5.1 Model for Bicycle Users 71

3.5.2 Model for Motorcycle Users 72

3.5.3 Estimation of WTP Values 73

3.6 GENERALIZED COST OF TRAVEL 74

3.7 SUMMARY 75

Chapter 4. Design of Feeder Service

4.1 INTRODUCTION 76

4.2 DATABASE 76

4.2.1 Travel Demand to Bus Stop 76

4.2.1.1 Modeling of Trip Rates 77

Revenue generating trips 77

Educational trips 78

Household trips 79

Total trips 80

4.2.1.2 Validation of Modeled Trip Rates 80

4.2.1.3 Estimation of Trips 81

4.2.2 Temporal Variation of Demand 82

4.2.3 Road Network 83

4.2.3.1 Base Network 83

4.2.3.2 Road Network to Bus Stop 83

4.2.4 Cutoff Revenue 85

4.3 MEASURES OF EFFECTIVENESS, ALTERNATIVE

SCENARIOS AND FARE LEVELS

87

4.3.1 Measure of Effectiveness 87

4.3.2 Alternative Scenarios 88

4.3.3 Fare Levels 90

4.4 FEEDER SERVICE WITH GENERALIZED COST AS MEASURE

OF EFFECTIVENESS

90


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Content

vi

Title Page

4.4.1 Feeder Routes and Vehicle 91

4.4.1.1 Scenario-I 91

4.4.1.2 Scenario-II 92

4.4.1.3 Scenario-III 93

4.4.1.4 Scenario-IV 93

4.4.1.5 Scenario-V 94

4.4.1.6 Scenario-VI 94

4.4.1.7 Comparison of Different Scenarios 95

4.4.2 Effect of ASC on Operational Viability 96

4.5 FEEDER SERVICE WITH PASSENGER-KM AS MEASURE OF

EFFECTIVENESS

100

4.5.1 Feeder Routes and Vehicle 100

4.5.1.1 Scenario-I 100

4.5.1.2 Scenario-II 101

4.5.1.3 Scenario-III 101

4.5.1.4 Scenario-IV 102

4.5.1.5 Scenario-V 103

4.5.1.6 Scenario-VI 103

4.5.1.7 Comparison of Different Scenarios 104

4.5.2 Effect of ASC on Operational Viability 105

4.6 FORMS OF OPERATION OF FEEDER VEHICLES 108

4.7 SUMMARY 111

Chapter 5. Conclusions

5.1 INTRODUCTION 112

5.2 CONCLUSION 112

5.3 FUTURE SCOPE OF THE WORK 117

References 119

Annexure-A 137

Annexure-B 153

Resume


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Content

vii

LIST OF TABLES

Table No. Title of Table Page

Table 1.1 Descriptive Statistics of the Study Area 7

Table 3.1 Attributes and Their Levels 50

Table 3.2 Salient Features of Data Used for Model Development 55

Table 3.3 Estimation Results of MNL Models (Stage-I) 57

Table 3.4 Estimation Results of DGNL Model 58

Table 3.5 Estimation Results of NL Models 60

Table 3.6 Estimation Results of CHNL Models 62

Table 3.7 Estimation Results of RPL Models (Stage-I) 64

Table 3.8 WTP Estimates from RPL1 Model (Stage-I) 65

Table 3.9 Estimated WTP Values from Stage-I Models 66

Table 3.10 Log-likelihood Test of Logit Models (Stage-I) 69

Table 3.11 Estimation Results of Models for Bicycle Users 72

Table 3.12 Estimation Results of Models for Motorcycle Users 73

Table 3.13 Estimated WTP Values from Stage-II Models 74

Table 4.1 Average Income for Different Categories of Household 77

Table 4.2 Estimated Trip Rates: Revenue Generating Trips 77

Table 4.3 Estimated Trip Rates: Educational Trips 78

Table 4.4 Bus Stops and Their Influence Areas 84

Table 4.5 Fixed Cost of Feeder Vehicles 86

Table 4.6 Revenue Required for Covering Fixed Cost 86

Table 4.7 Revenue Required for Covering Running Cost 87

Table 4.8 Selected Fare Combinations 90

Table 4.9 Details of Feeder Service in Scenario-I: GC as MOE 91

Table 4.10 Summary of Feeder Service in Scenario-I: GC as MOE 92

Table 4.11 Summary of Feeder Service in Scenario-II: GC as MOE 93

Table 4.12 Summary of Feeder Service in Scenario-III: GC as MOE 93

Table 4.13 Summary of Feeder Service in Scenario-IV: GC as MOE 94

Table 4.14 Summary of Feeder Service in Scenario-V: GC as MOE 94Table 4.15 Summary of Feeder Service in Scenario-VI: GC as MOE 95

Table 4.16 Attributes of Recommended Feeder Service: GC as MOE 96

Table 4.17 Effect of ASC on Recommended Feeder Service in

Scenario-I: GC as MOE

97

Table 4.18 Effect of ASC on Revenue Surplus: GC as MOE 100


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Content

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Table No. Title of Table Page

Table 4.19 Summary of Feeder Service in Scenario-I:Passenger-km as MOE

101

Table 4.20 Summary of Feeder Service in Scenario-II:Passenger-km as MOE

101

Table 4.21 Summary of Feeder Service in Scenario-III:Passenger-km as MOE

102

Table 4.22 Summary of Feeder Service in Scenario-IV:

Passenger-km as MOE

102

Table 4.23 Summary of Feeder Service in Scenario-V:

Passenger-km as MOE

103

Table 4.24 Summary of Feeder Service in Scenario-VI:

Passenger-km as MOE

104

Table 4.25 Attributes of Recommended Feeder Service:

Passenger-km as MOE

105

Table 4.26 Effect of ASC on Revenue Surplus: Passenger-km asMOE 107

Table 4.27 Comparison of GC Saving for Bus Stop Bound Trips 110

Table 4.28 Comparison of GC Saving for Both Directions of Travel 111


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Content

ix

LIST OF FIGURES

Figure No. Title of Figure Page

Figure 1.1 Feeder Vehicles Considered in the Present Work 5

Figure 1.2 Outline of the Study Area 7

Figure 2.1 Schematic Diagram for TravelBehaviour Analysis 10Figure 2.2 Schematic Diagram for Design of Feeder Service 36

Figure 2.3 Selection of Routes and Vehicle with ExternalSubsidy 41

Figure 2.4 Selection of Routes and Vehicle with Cross Subsidy 45

Figure 3.1 Three Level Form of Operation Based Tree Structure 59

Figure 3.2 Three Level Vehicle Based Tree Structure 60

Figure 3.3 Two Level Form of Operation Based Tree Structure 60

Figure 3.4 Two Level Vehicle Based Tree Structure 60

Figure 4.1 Comparison of Modeled and Observed Revenue

Generating Trips

81

Figure 4.2 Comparison of Modeled and Observed Household Trips 81

Figure 4.3 Estimated Trips to Bus Stops 82

Figure 4.4 Temporal Variation of Travel Demand 83

Figure 4.5 Road Network to Bus Stops 85

Figure 4.6 Comparison of Different Scenarios: GC as MOE 95

Figure 4.7 Effect of ASC on GC Saving: GC as MOE 98

Figure 4.8 Effect of ASC on Passenger-km: GC as MOE 98

Figure 4.9 Effect of ASC on Passenger Served: GC as MOE 98

Figure 4.10 Stability of Routes with GC as MOE 99

Figure 4.11 Comparison of Different Scenarios: Passenger-km as 104

Figure 4.12 Effect of ASC on GC Saving: Passenger-km as MOE 105

Figure 4.13 Effect of ASC on Passenger-km:Passenger-km as MOE 106

Figure 4.14 Effect of ASC on Passenger Served: Passenger-km as 106

Figure 4.15 Stability of Routes with Passenger-km as MOE 107


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Chapter 1

Introduction

1.1 MOTIVATION

A countrys ability to unleash economic potential is closely linked to the

effectiveness of its transportation system. Over several decades, lack of

transportation infrastructure has been a major bottleneck for accelerating

the economic growth of developing countries. In the recent years,

developing countries like India have realized the need for roadinfrastructure, and taken up several highway development projects (Maitra

et al. 2002; 2003). As rural population constitutes around 70% of the

countrys population, Government of India also initiated a rural road

development programme, in the year 2000, called Pradhan Mantri Gram

Sadak Yojana (PMGSY) to connect all villages with a population of more

than 500 by all weather roads. It is aimed to provide single all weather

road connectivity to 160,000 villages in India by construction of rural roads

under PMGSY (Sikdar 2002).

Private vehicle ownership is very low in rural India. Therefore, alongwith the

improvement of road connectivity, it is also necessary to develop suitable

passenger transportation systems in rural areas. Presently, most of the

major roads like National Highways, State Highways and Major District

Roads are served by bus transportation systems. However, transportation

linkage between village settlements and bus stops is largely a missing

component.

With the development of rural roads, paratransit carriers have started

operating in some areas for providing access to nearest bus stop or higher

order settlements. However, a systematic approach for the development of

feeder service, based on planning principles, is missing. Several studies


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Chap te r 1

2

have been reported in literature on routing, scheduling and design of feeder

service in urban areas (Wirasinghe 1980; Geok and Perl 1988; Martins and

Pato 1998; Shrivastava and Dhingra 2001; Shrivastava and OMahony

2006).However, there is little information available regarding planning of

rural feeder service in developing countries. While in rural areas the

demands are scattered over large geographical areas, the socioeconomic

characteristics of rural population are also distinctly different from urban

population. Therefore, it is necessary to develop a systematic approach for

planning of rural feeder service with due consideration to demand pattern

and socioeconomic characteristics of trip makers.

Users benefit is a driving force for improvement planning of transportation

system. For estimation of benefit, it is required to understand users

valuation of travel attributes or willingness-to-pay (WTP). Several studies

have been reported in literature on value of travel time savings (Bradley

and Gunn 1990; Carlsson 1999; Hensher 1994; Hensher 2001a; Hess et al.

2005). In some cases, valuation of qualitative travel attributes has also

been attempted (Hensher and Sullivan 2003; Hunt 2001; Phanikumar and

Maitra 2007). However, there is very little information available regarding

valuation of travel attributes by rural trip makers in developing countries.

Therefore, in the process of planning of rural feeder service it is also

necessary to investigate trip makers valuation of travel attributes or WTP.

1.2 OBJECTIVE AND SCOPE OF WORK

The objective of the present work is to develop a framework for planning of

rural feeder service to bus stop. In order to satisfy the objective, it is

necessary to address several related aspects like type of feeder vehicle,

form of operation, estimation of demand, estimation of users benefit,

selection of routes, fare level and operational viability. The carrying

capacity, fixed cost and operating cost are different for different vehicle

types. Accordingly, service characteristics like headway and fare, and

operational viability are also likely to be influenced by the selection of

feeder vehicle. In the present work, two small carriers are considered as


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I n t r o d u c t i o n

3

feeder vehicles. The users benefit resulting from feeder service may be

influenced by the form of operation of feeder vehicles. In the present work,

along with the traditional fixed-schedule form of operation, two demand-

responsive forms of operation of feeder vehicles are investigated. The

demand-responsive forms are called as dial-a-ride and dial-a-slot.

Investigation on travel behaviour of trip makers is an integral part of

planning of rural feeder service. It is necessary to carry out travel behavior

analysis for rational estimation of users benefit and estimation of demands

to be served by feeder service. It is a common practice to design survey

instrument, collect behavioral data (stated preference and/or revealed

preference) from commuters, and analyze the same using suitable model

specifications. Although behavioral data can be analyzed by different model

specifications, the scope of the present work is limited to the use of three

logit model specifications namely Multinomial Logit (MNL), Nested Logit (NL)

and Random Parameter Logit (RPL).

Users benefit in the context of transport improvement may be perceived as

a reduction in the disutility of travel. Disutility of travel is generally

expressed using several quantitative and qualitative attributes. These

attributes have different measuring units, and therefore, need to be

transformed to have a common unit for comparison or aggregation purpose.

When monetary attribute is involved, the transformation is simple and the

transformed value associated with each attribute is termed as WTP.

Aggregation of WTP values for all the attributes, describing an alternative, is

termed as Generalized Cost (GC). A reduction in the GC may be considered

as a measure of users benefit.

The WTP values may be influenced by the socioeconomic and/or trip

characteristics of trip makers. Therefore, it is necessary to investigate the

effect of socioeconomic and/or trip characteristics on WTP values. As a part

of the total demand is expected to use feeder service, it is necessary to

model total passenger demand from each village settlement to bus stop.


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Chap te r 1

4

The selection of route, vehicle type and form of operation are also required

to be judged in the light of operational viability and other policy measures.

The scope of the present work is summarized as follows:

Travel Behaviour Analysis

Design of experiments Collection of stated preference data Development of utility equations using different Logit model

specifications:

o Multinomial Logito Nested Logito Random Parameter Logit

Estimation of willingness-to-pay values Comparison of models obtained from different Logit model

specifications

Selection of models for estimation of ridership Development of generalized cost equations

Design of Feeder Service

Development of Database Selection of feeder routes and vehicles Comparison of different forms of operation of feeder vehicles

o Fixed-scheduleo Dial-a-rideo Dial-a-slot

The work is demonstrated with reference to a case study in rural India.

1.3 FEEDER VEHICLES AND FORMS OF OPERATION

Rural settlements are located in a scattered manner covering large

geographical area. As a result, demand levels are generally low on various

roads connecting rural settlements to the nearest bus stop. In low dispersed

demand scenario, only small carriers are considered suitable as feeder

vehicles. Two alternative vehicles are considered (Figure-1.1): Tempo with

carrying capacity of 6 persons and Trekker with a carrying capacity of 10


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5

persons. It may be mentioned that both capital cost and operating cost are

low for Tempo and Trekker. Therefore, these vehicles are widely used as

paratransit modes in India.

Tempo Trekker

Figure-1.1 Feeder Vehicles Considered in the Present W ork

Alongwith the traditional fixed-schedule form of operation, two demand-

responsive forms of operation of feeder vehicles namely; dial-a-rideand

dial-a-slot are investigated. In fixed-schedule, the arrival time of the

next vehicle is known to commuters but the availability of seat is not

assured due to limited seat capacity. As the next vehicle arrival time is

known but the seat availability or travel opportunity in that vehicle is not

assured, commuters waiting time is described as anxious waiting at stop.

In dial-a-ride, commuters are assumed to inform service provider about

the origin and the destination for a ride along the route using toll free

telephone available at stop provided by Government or service provider. In

response, service provider informs commuters about the vehicle allotted

for trip, but starts the vehicle only when the capacity utilization of the

vehicle along the route is assured to a desired level. Therefore, both

operator and commuters are benefited. The operator provides the service

with desired utilization of seat capacity and commuters are benefited as

the seat availability is assured in a specified vehicle. As the seat availability

is assured in a specified vehicle, the waiting is described as Relaxed

Waiting at Stop.


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Chap te r 1

6

In dial-a-slot, the span of operation is divided into suitable time slots.

Commuters are assumed to inform service provider in advance about their

preferred time slot for journey by dialing a toll free telephone number from

home. The service provider considers all such requests, schedules a vehicle

ensuring acceptable usage of vehicle capacity along the route, and informs

commuters about the allocated time slot and vehicle. In the process, some

commuters may be allocated time slots other than the requested ones.

Deviation from requested time slot, if any, is considered as disutility to

commuters. As the seat availability is assured in a specified vehicle and

arrival time is also known, commuters can wait at home. The time

deviation or waiting is considered as Relaxed Waiting at Home. In this

form of operation, both operator and commuters are benefited. The

operator provides the service with acceptable utilization of seat capacity

along the route, and commuters are benefited as the seat availability is

assured and waiting is a relaxed-waiting at home.

1.4 STUDY AREA

A 194.3 square-km geographical area in the state of West Bengal, India is

selected as the case study. The study area includes parts of three

administrative blocks namely Narayangarah, Kesiary and Dantan-I in

Kharagpur Subdivision of West Medinipur District. The study area is located

outside the urban fringe and represents typical rural characteristics. The

nearest urban centre (i.e. Kharagpur town) is about 37 km from the north-

eastern corner, and 26 km from the north-western corner of the study

area. The study area is bounded by National Highway-60 (NH) in the

Eastern side, BeldaKesiary road (major district road, MDR) in Northern

side, KesiaryBhasra road (major district road, MDR) in Western side and

river Subarnarekha in the Southern side (Figure-1.2). The NH and MDRs

are served by bus service. However, the roads within the study area are

not served by public transport system and therefore, a distinct travel

patterns is observed from village settlements to bus stops located on roads

representing the study area boundary. Descriptive statistics of the study

area are shown in Table-1.


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7

Figure-1.2 Outline of the Study Area

Table-1 Descriptive Statistics of the Study AreaNumber of villages* 173

Number of households* 22,434

Total population* 115755

Average household size* 5.16

Rate of literacy* 58%

Total population with age less than 6 years* 17142

Total working population* 45208

Total main work force* 30517

Agriculture dependable main work force*

68.96%Casual work force* 14661

Agriculture dependable casual work force* 89.3%

Number of villages at a distance beyond 2 km from bus stop 126

Population living beyond 2 km from bus stop 74401

Number of households beyond 2 km from bus stop 14274


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Chap te r 1

8

Road length (within the study area) 253.9km

National Highway (a part of the study area boundary) 29.1km

Major District Road (a part of the study area boundary) 21.9km

*Source: 2001 census, Govt. of India

1.5 ORGANIZATION OF REPORT

The motivation, objective and scope of the present work along with a brief

description of feeder vehicles and forms of operation are discussed in

Chapter 1. A description of the study area is also included in Chapter 1.

Chapter 2 deals with the approach and methodology followed for travel

behaviour analysis including preference elicitation, selection of attributes

and their levels, generation of alternatives, data collection and analysis.

Besides, it also includes a discussion on the approach adopted for the

selection of feeder routes and vehicles. The approach followed for the

comparison of different forms of operation of feeder vehicles is also included

in Chapter 2. Chapter 3 presents in details the econometric analysis of

behavioral data using various model specifications, and estimation of trip

makers willingness-to-pay with respect to attributes of hypothetical feeder

service. Chapter 4 demonstrates estimation of demand, selection of feeder

routes and vehicles under different policy scenarios, and comparison of

different forms of operation of feeder vehicles. Chapter 5 presents

conclusive remarks and the findings from the work, with a note on scope of

future works.

1.6 SUMMARY

This chapter justifies the need for developing a framework for planning of

feeder service to bus stop. The objective of the study is outlined and the

steps required to be carried out to fulfill the objective are enumerated under

the scope of the work. The types of feeder vehicle and forms of operation

considered for investigation are also discussed. The study area is described

and the organization of report is also mentioned.


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Chapter 2

Approach and Methodology

2.1INTRODUCTION

This chapter presents the approach and methodology followed to develop a

framework for planning of rural feeder service to bus stop. There are two

major components of work namely Travel Behaviour Analysis and Design

of Feeder Service. Section 2.2 describes methodology followed for travel

behaviour analysis, which includes selection of attributes and their levels,preparation of choice sets, design of questionnaire, collection and

organization of data, analysis of data using different model specifications,

estimation of willingness-to-pay (WTP) values and development of

generalized cost (GC) equations. The methodology followed for design of

feeder service is described in Section 2.3. This section includes development

of database required for design of feeder service, measure of effectiveness

for selection of feeder routes and vehicles, policy instruments, and

comparison of different forms of operations of feeder vehicles. Finally, thework presented in this Chapter is summarized in Section 2.4.

2.2 TRAVEL BEHAVIOUR ANALYSIS

The travel behaviour analysis includes selection of attributes and their

levels, preparation of alternatives, formation of choice sets, collection of

data and development of database, analysis of behavioural data using

different model specifications, estimation of WTP values and development of

generalized cost equations. Figure-2.1 shows a schematic diagram of the

methodology followed for travel behaviour analysis. Various steps of the

methodology are discussed in subsequent sections.


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Chapt er 2

10

Figure-2.1 Schematic Diagram for Travel Behaviour Analysis

2.2.1 Design of Experiment

Design of experiment aims to combine attribute levels into profiles of

alternatives and choice sets. It provides a structure that allows estimation

of choice parameters in models and a highly structured method of data

generation (Hanley et al. 1998), relying on carefully designed tasks or

experiments to reveal the factors that influence choice. In a choice

experiment, individuals are asked to choose their preferred alternative

among several alternatives presented in a choice set. Each alternative is

described by a number of attributes or characteristics. A monetary value is

included as one of the attributes, along with other attributes of importance,

while describing the profile of the alternative presented. Few alternative

profiles are then assembled in a choice set and presented to respondents,

who are asked to state their preferred profile in each choice set (Hanley et

al. 1998; Louviere et al. 2000; Bennett and Blamey 2001). Thus, when

individuals make their choice, they implicitly make trade-offs among the

levels of the attributes in the different alternatives presented in a choice

set. The attribute in monetary form enables estimation of the value of the

Design of Experiment

Type of Data and Preference Elicitation Technique,Attributes and their Levels, Choice Sets,Questionnaire Design and Pilot Survey

Selection of Models for Estimation of Ridership

Development of Generalized Cost Equations

Collection of Data and Development of DatabaseSampling Strategy, Data Collection and Database Development

Analysis of Data

Organization of Data, Econometric Models

Valuing the Attributes/ w illingness-to-pay (WTP ) values

Comparison of utility equations


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other attributes in terms of respondents WTP. So, design of experiment

includes type of data, preference elicitation method, attributes and their

levels, design of alternatives and choice sets.

2.2 .1 .1 Type o f Data and Pre fe rence El ic i ta t ion Techn iq ue

In behavioural analysis, it is necessary to collect preferences of people in

the form of either Revealed Preference (RP) or Stated Preference (SP) data

(Adamowicz et al. 1994; Bates 1982; Kroes and Sheldon 1988; Louviere

1988a; Hensher 1994; Holguin-Veras 2002). RP refers to the observation of

preferences revealed against real articles. It is required to have a present

demand for the article in question in order to apply RP. In RP, the attributes

may be collinear, making it difficult or impossible to predict the effect of

independent variation in an attribute. Also, RP data may be inappropriate as

they cannot accommodate non-existing attributes or variability of attributes

which in-turn does not permit to establish their influences. RP methods

capture only use value. In addition, RP data requires large number of

observations leading to expensive and time consuming data collection

process. RP methods include approaches such as the hedonic pricing

(Pommerehne 1988; Bateman et al. 2000; Howarth et al. 2001) and the

travel cost method. In the hedonic pricing method, an article is assumed to

be formed by a set of attributes and the value of article is considered as a

function of each attribute. The value of an attribute is called an implicit price

or a hedonic price of the attribute, as it cannot be observed directly in a real

market. It is possible to estimate the price by analyzing the prices of an

article that has different quantities of each attribute in the market. Basic

problems with the hedonic pricing method include omitted variable bias,

multicollinearity, functional form, market segmentation and restrictive

market assumptions. In the travel cost method, a value of non-market

article is estimated by using consumption behaviour in a related market,

where travel costs are used as a measure of preferences for the article.

Basic problems with the travel cost method include choice of dependent

variable, multi-purpose trips, holiday-makers versus residents, calculation

of distance costs and the value of time, and statistical problems.


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SP refers to observation of preferences stated against real and/or

hypothetical article. SP facilitates inclusion of hypothetical attributes and

variability of attributes and requires fewer observations than RP. In

addition, it allows complete control over choices offered and their attributes

and ensures sufficient variation in data. SP methods include Contingent

Valuation Methods (CVM) and Conjoint Methods (Pommerehne 1988). In

CVM, the entire article is valued by eliciting a persons WTP directly. As a

result, nothing is revealed about the value of the different attributes that

comprise the article. CVM may be classified as open ended CVM and

referendum CVM. In the open ended CVM, a person is asked to state his/her

WTP without giving any amount. The WTP can be estimated by simply

taking the mean of the WTP stated. In the referendum contingent valuation,

an amount is given and a person is asked to state whether he is willing to

pay or not (yes/no). The data is generally analyzed using binary logit

model.

Conjoint Methods include Conjoint Rating, Ranking and Choice (Discrete

Choice Experiment). These three methods differ in theoretical assumptions,

methods of analysis and experimental procedures (Louviere et al. 2000;

Blamey et al. 2002). The basic design of alternatives is same in the threetechniques and respondents must decide which of mutually exclusive multi-

attribute alternative(s) they prefer. In Rating, respondents are asked to

evaluate a series of alternatives, one at a time, using a numerical ratings

scale. The degree of task complexity is higher as the respondents have to

place a value (characterizing the strength or degree of preference) on each

alternative (Louviere et al. 2000). Contingent rating provides the

respondent with the opportunity to rate alternatives equally and thus to

indicate indifference among alternatives. The data is analysed by regressingthe rating scores against the attributes and using Ordinary Least Squares

(OLS), to estimate regression parameters. Ratings data are ordinal (only

the ordering matters: the difference in ratings does not measure the

strength of preference for one alternative over another) and discrete (as

opposed to continuous variables) and these characteristics of the data


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violate the assumptions underlying OLS. Furthermore, because respondents

are not required to choose a particular alternative, but to simply rate each

one on a preference scale, the model cannot be used to predict choice

behaviour or level of demand for a particular alternative (Adamowicz et al.

1998). There exists a second variant of rating method called paired

comparison, where respondents are presented two alternatives and asked

to rate their preference for the alternatives on a five or ten-point scale

(Likert scale e.g., 1= highly preferred, 5= highly not preferred). These

numbers may not represent the actual or true choice behavior of individuals

due to the lack of strong theoretical foundation consistent with economics

(Adamowicz et al. 1998). In most applications it is a standard practice tovary both levels and types of attribute over the series of questions, such

that respondents are only required to consider two or three attributes at a

time.

In Ranking, respondents are presented with three or more alternatives in

one question and asked to rank the alternatives from most to least

preferred, therefore, provide a complete preference order (strongly

ordered). A series of these ranking exercises is administered to the

respondent. Conjoint ranking is not much used because of theoretical

difficulties in analyzing the data (Louviere and Timmermans 1990).

The use of ranking and rating techniques suffers from potential theoretical

and practical obstacles. These concerns include the difficulty individuals

might experience in ranking/rating all the alternatives, and the fact that

rating tasks in particular involve difficulty in making interpersonal

comparisons and departure from the choice contexts that are faced by

consumers in the real world (Bennett and Blamey 2001). Several SP studies

used traditional ranking or rating based preference techniques (Hunt 2001;

Lai and Wong 2000; Praveen and Rao 2002).

Discrete choice experiment (DCE) is the simplest of the choice techniques

and thus its biggest advantage is the low cognitive complexity the degree


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of task complexity and difficulty arising from the experiment. The DCE

experiments provide a framework for estimating the relative marginal

disutility of variations in attributes, and their potential correlations

(Louviere et al. 2000). The DCE method involves consumers making

mutually exclusive choices from a set of substitutable articles. Moreover,

DCE has strong theoretical foundation based on economic theory, and is an

established approach for understanding and predicting consumer tradeoffs

and choices in marketing research. DCE methods have been used

extensively to model the behavior of individuals. For example, in the field

of transportation, for valuing travel time savings (Carlsson 1999; Hensher

2001a, 2001b; Hensher 2004; Hess et al. 2005; Greene et al. 2006), for

mode choice modeling (Bhat 1995; Brownstone and Train 1999;

Brownstone et al. 2000; Alpizar and Carlsson 2001; Arne and Felix 2004;

Train and Winston 2004; Koppleman and Sethi 2005), for route choice (Yai

et al. 1997; Hensher and Sullivan 2003), and several other non-

transportation fields such as households response to rebates on energy-

efficient appliances (Revelt and Train 1997), impact of fish stock, which is

affected by water quality, on anglers choice of fishing site (Train 1998)

recreational saltwater fishing site (Mellisa et al. 1998), tourism (Robin and

Adamowicz 2003), valuing wetland attributes (Carlsson et al. 2003),

demand for genetically modified food (Rigby and Burton 2003; Carlsson et

al. 2004; Onyango et al. 2004), and consumers willingness to pay for

water service improvements (Hensher et al. 2004; MacDonald. et al.

2003), to avoid power outages (Carlsson and Martinsson 2004), radical

Islamic terrorism (Barros and Proena 2005), British general elections

(Garrett 2001), voting behavior (Ding-Ming Wang 2001), etc. The attribute

based DCE technique is therefore, adopted in the present study for

collecting the preferences of users.

The term discrete choice arose from the distinction between continuous

and discrete variables for denoting a set of alternatives. The word discrete

indicates that the choice is discrete in its nature, meaning that it is only

possible to choose one alternative. A discrete choice situation is defined as


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one in which the respondent faces a choice among a set of alternatives

meeting the following criteria (Train 1993):

The number of alternatives in the set is finite The alternatives are mutually exclusive The set of alternatives is exhaustive (all possible alternatives are

included)

The DCE is characterized as a method in which the article in question is

described by a number of attributes. The attributes and their levels must be

constructed so that they force the respondent to trade. It is important to

note that each time a level is changed, a new scenario arises. By securing a

certain variation in the scenarios, it becomes possible to examine the

degree to which each attribute influences the choice of the decision-maker;

that is, to estimate the marginal rates of substitutions of the attributes

(Louviere et al. 2000). Attributes can possess either positive or negative

utility, and to varying degrees. Choice experiments can thus be used to

examine the response of an individual to changes in the scenario attributes.

2 .2 .1 .2 A t t r i bu t es and The i r Leve ls

For the development of choice sets, it is necessary to identify suitable

attributes and define their levels. A well-designed behavioral experiment

requires significant pre-testing for identifying attributes, their levels and

important interactions (Louviere 1988b). Bennett and Blamey (2001)

specified that the attributes should be relevant to the requirements of the

policy makers, but need to be meaningful and important to the respondents.

To satisfy these requirements, it is important to carry out review of

literature, group discussions, and interviews with key persons such as policy

makers, experts and users. Blamey et al. (2002) suggested that the

preference should be given to those attributes which are demand-relevant,

policy relevant and measurable. The cost attribute plays an important and

distinct role in the DCE. The inclusion of a cost attribute provides the DCE

with a special quality as it becomes an elicitation procedure for WTP. This

implies that benefits are estimated in monetary terms and causes the DCE

to be consistent with welfare economics. Results from different studies can


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then be compared. Inclusion of a cost attribute makes it possible to

indirectly obtain the respondents WTP for either the article in its entirety

(an alternative) or the respondents WTP for the attribute respectively, i.e.

marginal WTP (Bennett and Blamey 2001; Carlsson et al 2003; Hanemann

1984)

In this context, it is important to determine the way in which levels are to

be presented: either qualitatively or quantitatively. Moreover, it is

necessary to decide whether the quantitative attributes should be

presented in absolute or relative terms. Green and Srinivasan (1990)

mentioned that the levels have to be acceptable such that levels that will

be dominated at any stage are avoided. Ryan (1999) described three key

factors while choosing the levels for each attribute:

The levels must be plausible to the respondents The levels must be actionable to the respondents The levels must be constructed so that the respondents are willing to

make trade-offs between combinations of the attributes.

In the present study, alternatives of feeder services are designed on the

basis of type of feeder vehicle and form of operation. The attributes of two

vehicles and three forms of operation as discussed in Chapter 1 are taken

for the design of alternatives. The significant attributes and their levels for

generation of alternatives are selected based on discussions with trip

makers and experts.

2.2.1 .3 Choi ce Set s

This stage includes the formation and pairing of alternatives. Various

methods can be used to design and reduce (if required) the number of

alternatives to be included in the questionnaire. One of the crucial

objectives of the experimental design is that the number of alternatives is

minimized while being able to infer utilities for all possible alternatives

which imply keeping the choice task simple to the respondents and at the

same time being able to extract all the necessary information from the


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choices. Design of experiment is a way of manipulating attributes and their

levels to permit rigorous testing of certain hypotheses of interest (Louviere

et al. 2000). The most popular way of combining the levels of the

attributes is the factorial design. The questionnaire is designed so that

each level of each attribute is combined with every level of all other

attributes. A factorial design is simply the factorial enumeration of all

possible combinations of attribute levels. Factorial design may be full

factorial and fractional factorial.

Full factorial design refers to a design in which all possible alternatives are

represented. If there are M attributes each with L levels, then the full

factorial technique would result into LM alternatives. Full factorial design

has very attractive statistical properties as it guarantees that all attribute

effects of interest are truly independent (i.e. attributes are independent by

design). However, full factorial design is only a real possibility for small

experiments that involve a limited number of attributes or levels. With

more attributes and levels, it may be necessary to reduce the size of the

design. This can be done by the use of fractional factorial design (Louviere

et al. 2000).

Fractional factorial design involves selection of a subset (a fraction) of the

full factorial design, in which the properties of the full factorial design are

maintained in the best possible manner, such that the effects of interest

can be estimated as efficiently as possible. However, all fractional designs

involve some loss of statistical information. This loss of information can

sometimes be significant, as fractional factorial designs limit the ability to

take higher order effects into account, i.e. interactions among two or more

attributes (Louviere et al. 2000). Several studies used main effect

fractional factorial design, in which it is assumed that interactions among

attributes are insignificant in all two-way and higher order interactions.

Louviere et al. (2000) stated that the exclusion of interaction effects does

not necessarily lead to biased result, because:

Main effects typically account for 70% - 90% of the explained variance


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Two-way interactions typically account for 5% - 15% of the explainedvariance

Higher-order interactions account for the remaining explained variance.Even if interactions are significant, they rarely account for much of the

explained variance and hence may not significantly affect design efficiency.

When it comes to the efficiency of designs, Huber and Zwerina (1996)

mentioned that level balance, minimal overlap, utility balance and

orthogonality are the four properties that characterize efficient choice

designs. Level balance requires that the levels of each attribute occur with

equal frequency in the design. A design has minimal overlap when an

attribute level does not repeat itself in a choice set. Minimal overlap relates

to the statistical properties when pairing the alternatives. Utility balance

requires that the utility of each alternative within a choice set is equal.

Utility balance is difficult to incorporate in the design as it demands a priori

knowledge of respondents preferences. Finally, orthogonality can be

considered as the most important aspect of efficiency of experiments. An

orthogonal design is one in which the levels of different attributes across

profiles are uncorrelated. Such designs assure that an estimate of one

attribute is unaffected by the estimate of other attributes (Huber 1987).

The type of design or appearance of choice sets in the questionnaire is

another issue to be given due consideration. Three types of designs are

available for preparing choice sets: designs with fixed, randomized or

individualized choice sets. In a fixed experiment approach, each

respondent faces exactly the same choice sets at exactly the same stage of

the choice task. In a randomized experiment, each respondent also

receives the same choice sets but the order differs over respondents. In an

individualized experiment each respondent receives his/her own choice

sets, generally, pivoting on his/her previous responses. Depending on the

data collection method and analysis, either of the design types may be

adopted.


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Another issue is whether to present the alternatives in the choice sets in a

generic (alternatives A, B, C) or alternative specific form (Brand A, Brand

B, Brand C). Blamey et al. (2000) discussed advantages of these two

approaches. An advantage of using alternative specific labels is familiarity

with the context and hence the cognitive burden is reduced. However, the

risk is that the respondent may not consider trade-offs between attributes.

This approach is preferred when the emphasis is on valuation of the labeled

alternatives. An advantage of the generic model is that the respondent is

less inclined to only consider the label and thereby focus more on the

attributes. Therefore, this approach is preferred when the emphasis is on

the marginal rates of substitution between attributes. It is necessary to

check the questionnaire with the help of a pilot study and redesign the

questionnaire, if necessary.

In the present study alternatives are generated using fractional factorial

orthogonal main effects only technique. The choice sets are prepared using

fixed experiment approach in alternative specific form.

2.2 .1 .4 Quest ionna i r e Des ign and P i lo t Surv ey

The questionnaire should include socioeconomic and trip characteristics of

respondents as well as the SP choice sets. Before going for the main

survey, the design questionnaires should be taken for pilot survey to see

the understanding of targeted population. Considering low literacy rate,

and exposure of the people of the area to such type of survey for the first

time, an added emphasis is given in the present work on pilot surveys.Process of presenting choice set, number of choice set in the questionnaire,

etc. are finalized based on pilot surveys.

2.2.2 Collection of Data and Development of Database

Before carrying out data collection, it is necessary to resolve the issue of

sampling. All surveys need to be based on the application of strict sampling

theory (Kish 1965; Yates 1981). This permits quite small samples to be

reprehensive of population from which the samples are drawn. Without


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representativeness, it would normally necessary to conduct a complete

census, which is an expensive option, probably to the point that

transportation data would never be collected. The choice of survey

population obviously depends on the objective of the survey. Given the

survey population, a sampling strategy should be determined. Sampling

strategy may indicate either simple random sampling or choice based

sampling. Random sampling is one in which all individuals from the sample

have equal opportunity to be chosen as potential respondents. Choice

based sampling/ stratified sampling is one in which individuals are chosen

depending upon characteristics/ strata such as gender, income, occupation,

residential location, etc. (Louviere et al. 2000). The motivation for

randomness is that if the sample is random, the sampling distribution will

be the normal distribution with the true mean of the population. If the

sample is not random, a bias is introduced which causes a statistical

sampling or testing error by systematically favoring some observations

over others.

When conducting a DCE, it is important to consider the data collection

procedure. The methods available for collecting data are (Bennett and

Blamey 2001): Face-to-face interview, telephone interview, mailed

questionnaires, email/internet, gathering in central facilities and

combination of the above. Face-to-face interviews are characterized by the

interviewer and respondents sharing both time and space. Besides

generating very high response rates, the advantage of this method is that

the interviewer can lead the respondent through the hypothetical scenario

and elaborate if the respondent does not understand the task. All other

techniques involve low response rate, high costs and time, especially in

developing countries.

In the present study, stratified random sampling based on occupation of

head of household is accepted for conducting survey. A face-to-face

personal interview with head of the household is adopted to collect the data.

The main reason for selecting this sampling method is due its capacity to


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include different strata of people along with maintaining randomness in the

sample; thus contributing to minimum bias (Hensher 1994). In developing

country like India, walking is the most common form of travel for a distance

of up to 1 to 2 km (Iles 2005). Therefore, behavioral samples are targeted

from villages which are located beyond 2 km from bus stops.

A proper database needs to be prepared with the data collected from the

surveys for an easy and efficient estimation of models and other analysis.

The database to be developed should include both primary and secondary

data. Primary data include socioeconomic and trip making characteristics of

respondents along with stated choice responses. Trip making data from

households for revenue generating and non-revenue trips are required to be

collected from primary sources. Secondary database should include bus

routes, road network and village level characteristic of the study area.

2.2.3 Analysis of Data

Analysis of data includes organization of data, model specifications, model

development and interpretation of results.

2 .2 .3 .1 Organ iza t i on o f Da ta

Data consisting of socio-demographic variables, preferences and other

details should be well organized. Generally, each choice contains

information related to the level of each attribute in the alternative, and the

chosen alternative. Several coding styles are available to decide how levels

of attributes enter into the models. Numerical/Quantitative attributes (e.g.,

time, price) can enter in cardinal linear form (i.e. on a continuous scale)

and take actual values. Qualitative attributes on the other hand can take

either dummy coding (0, 1) or effects-type coding (1, 0, -1) specifications.

In dummy coding, the presence of level in the design is coded as 1 and

absence as 0 whereas in effects-type coding, presence of level in the

design is coded as 1 and absence as -1 when there are two levels; and

presence of the first level as (1, 0) the second as (0, 1) and the third as (-

1, -1) in case of a three level attribute. This kind of coding has an


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advantage that all effects are stated in deviation from some average; and

interaction terms, if present in the design, are uncorrelated with the main

attributes. Also, the sum of the part worth across attributes is zero (Haaijer

1999).

2.2 .3 .2 Econom etr ic Mode ls

Commonly used models to estimate discrete choice experiments are Logit

and Probit models. Based on their evaluation technique they are further

categorized as closed form, partial closed partial simulation and complete

simulation (Train 2003), as given below:

Complete closed form Binary Logit /Multinomial logit (MNL)

/Nested logit (NL)

Partial closed form/ partial simulation Mixed logit, (ML) / (RPL)

Complete simulation Binary Probit/Multinomial probit

Heteroscedastic extreme value (HEV)

The scope of the present work is limited to analyzing behavioural data

using different logit model specifications namely MNL, NL and RPL.

Therefore, only these model specifications are introduced in this Chapter.

The theoretical foundation related to the development of these econometric

models is discussed in Annexure-A.

Multinomial logit (MNL):

In econometric models based on Random Utility Theory (Thurstone 1927;

McFadden 1974), the utility of each element consists of an observed

(deterministic) component and a random (disturbance) component. If the

random error terms are assumed to follow extreme value type-I (Gumbel)

distribution, and be independently and identically distributed (IID) across

alternatives and cases (or observations), the multinomial (or conditional)

logit (MNL) model (McFadden 1974) is obtained. This model can be

estimated by maximum likelihood techniques, and is useful for modeling

choice behavior due to its simple form for choice probabilities. However,


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several limitations apply to this model. The most severe of these is the

Independence of Irrelevant Alternatives (IIA) property, based on the

assumption that the error terms are independent across alternatives,

choice sets, and respondents (Louviere and Woodworth 1983; Kamakura

and Srivastava 1984), which states that a change in the attributes of one

alternative changes the probabilities of the other alternatives in proportion.

This substitution pattern may not be realistic in all settings. Secondly, the

coefficients of all attributes are assumed to be the same for all respondents

in a choice experiment, whereas in reality there may be substantial

variability in how people respond to attributes.

Nested logit (NL):

The most widely known model which relaxes IIA of the MNL model is the

nested logit (NL) model which allows interdependence between the pairs of

alternatives in a common group (McFadden 1978; Ben-Akiva and Lerman

1985; Bo rsch-Supan 1990). In Nested Logit Model, the set of alternatives

are divided (and sub-divided) into exclusive groups (nests), where some

aspect only pertains to members of that particular group. Derivation of NL

model is based upon the assumptions of MNL, except that correlation of

error terms is assumed to exist among predefined groups of alternative.

Such error correlations arise if an observed factor influences the utility of

all members in the group. The NL model can be written as product of a

series of MNL choice models defining each level of tree structure. NL

relaxes IIA by organizing similar alternatives into groups and allowing

different correlation patterns between groups than within group. By

allowing correlation among subsets of utility functions, the IIA problem of

MNL is alleviated partially. It retains restrictions that alternatives in a

common nest have equal cross-elasticity and alternatives not in a common

nest have cross-elasticity as for the MNL. The NL model arises as a random

utility model in which the random component of utility has the generalized

extreme value distribution.


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NL model has been used in several studies for analyzing preference such as

new vehicle choice (Mccarthy and Tay 1998), debit, credit, or cash use for

gasoline purchases (Carow and Staten 1999), alternative models of

automobile purchases (Wojcik 2000), pre-work trip-making and home

departure time choice (Yun et al. 2000), airport and airline choice for

passengers departing from a large metropolitan area (Pels et al. 2000),

choice of a graduate business school (Montgomery 2002), combined-mode

choice, location choice, and route choice (Lo et al. 2004), air craft choice

(Wei and Hansen 2005).

Covariance heterogeneity nested logit (CHNL):

The NL model imposes restriction of equal correlation in random utility

components among nested alternatives across respondents. The degree of

(increased) sensitivity (i.e. cross elasticity) between the alternatives

present in one branch to alternatives present in another branch differs

based on socioeconomic characteristic of respondents. In general, ignoring

the variation of covariance among nested alternative across respondents

making choice (i.e. ignoring covariance heterogeneity in a nested logit

model) may produce biased and inconsistent estimates of variables.

Allowing heterogeneity across individuals in the covariance of nested

alternatives in the estimation of NL model leads to the development of

CHNL model (Bhat 1997).

Random parameter logit (RPL):

Modifications to the MNL model to overcome the limitations lead to the

development of RPL. RPL is a highly flexible model that can approximate

any random utility model (McFadden and Train 2000). RPL model allows for

a more heightened level of flexibility by specifying taste coefficients to be

randomly distributed across individuals (Revelt and Train 1998 ; Louviere et

al. 2000). The utility expression for RPL is the same as that for MNL model

except that the analyst may nominate one or more taste parameters

(including alternative-specific constants, i.e. ASCs) to be treated as random

parameters with the variance and mean to be estimated. The RPL form has


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important behavioral implications. The attributes with random parameters

induce a distribution around the mean that provides a mechanism for

revealing preference heterogeneity in the sampled population. This

heterogeneity takes the form of a random effects version of unobserved

heterogeneity that may be refined by making it a function of observed

variables such as income, sex, age, trip purpose, etc. This is a way of

revealing the specific sources of variation in unobserved heterogeneity

across a sampled population. RPL can also account for correlation among

alternatives. There are several advantages of RPL model specification.

The model does not exhibit the IIA property The model can be derived from utility maximizing behavior The model can account for uncontrolled heterogeneity in tastes

across respondents

The model allows the unobserved factors to follow any distribution The model (with normally distributed coefficients) can approximate

multinomial probit models

RPL models have been used for analyzing preferences in numerous

contexts, which include valuing of travel time savings (Algers et. al. 1998;

Carlsson 1999; Hensher 2001a ; Hensher 2001b; Hess et al. 2005; Cherchi

and Polak 2005), travel mode choice (Alpizar and Carlsson 2001; Arne and

Felix 2004) households response to rebates on energy-efficient appliances

(Revelt and Train 1997), the impact of fish stock, which is affected by

water quality, on anglers choice of fishing site (Train 1998), recreational

saltwater fishing site (Mellisa et al. 1998), tourism in Uganda (Robin and

Adamowicz 2003), valuing wetland attributes (Carlsson et al. 2003),

demand for genetically modified food (Rigby and Burton 2003; Carlsson et

al. 2004; Onyango et al. 2004), and consumers willingness to pay for

water service improvements (MacDonald et al. 2003; Hensher et al. 2004),

to avoid power outages (Carlsson and Martinsson 2004), consumers

choice of vehicle (Brownstone and Train 1999, Brownstone et al. 2000,

Train and Winston 2004), radical Islamic terrorism (Barros and Proena


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2005), British general elections (Garrett 2001), voting behavior in Taiwan

(Ding-Ming Wang 2001), etc.

Distribution of random parameters:

In RPL model, it is necessary to make an assumption regarding the

distribution of random parameters/coefficients. This assumption causes

much concern in RPL model development process. A specific distribution is

selected with a sense that the empirical truth somewhere lie in their

domain. The commonly used distributions are normal (Revelt and Train

1997; Algers et al. 1998; Carlsson 1999; Alpizar and Carlsson 2001;

Hensher 2001a; Carlsson et. al. 2003; Cherchi and Polak 2005), log-normal

(Revelt and Train 1997; Hensher 2001a; Alpizar and Carlsson 2001),

uniform and triangular (Revelt and Train 2000; Hensher and Greene 2001;

Train 2001; Garrett 2001). However, there are other distributions, like

Johnsons SB distribution (Hess et al. 2005), discrete distribution

(Chintagunta et al. 1991), which are also attempted by researchers. A brief

discussion on various distributions is also included in Annexure-A.

It may be mentioned that every distribution has its strength and weakness.

The weakness is usually associated with the spread or standard deviation of

the distribution. At its extremes include behaviorally unacceptable sign

changes for the symmetrical distributions. The lognormal has a long upper

tail. The normal, uniform, and triangular may give the wrong sign for some

parameters depending on the standard deviation. One appealing solution is

to make the spread or standard deviation of each random parameter a

function of mean. This way a conversion to truncated or constrained

distributions appears to be the most promising direction of research in the

future (Hensher and Greene 2003). For example, the usual specification of a

normal distribution is = +i is , where is the mean as s is the spread

(standard deviation) and i is the random variable. The constrained

specification would be = +i i so that the standard distribution is made

equal to the mean. The constraint specification concept can be applied to

other distribution also. For example, a constrained triangular distribution is


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a generalization of the uniform distribution, allowing for a peak in the

density function with two endpoints of the distribution are fixed as zero and

2*mean. It is bounded below zero, bounded above at a reasonable value

that is estimated and symmetric such that the mean is easy to interpret. It

is appealing for handling of attribute values/ WTP values (Hensher and

Greene 2001). Also with = +i i , where i has support from -1 to +1, it

does not matter if is negative or positive. A negative coefficient on

i simply reverses all the signs of the draws, but does not change the

interpretation. The advantages of constrained triangular distribution (say

where mean equals spread) may be enumerated as follows.

A constrained triangular distribution assures that the sign of the mean isconstant throughout the sample

Unlike normal or lognormal distributions this is bounded in nature whichresults in early convergence (less computational time)

Estimation of WTP value is simple as the impact of spread is negligibledue to constraint. Ratio of mean coefficient of any attribute over mean

coefficient of cost directly gives WTP unlike with normal or lognormal

distributions where standard deviation has significant effect on WTP.

In the present work, all RPL models are developed assuming constrainedtriangular distribution of random parameters.

Selection of points for RPL model:

The RPL model does not have a closed form expression (unlike the MNL

model) and therefore, it is approximated numerically through simulation by

the method of Simulated Maximum Likelihood (SML). Numerous procedures

have been proposed for taking intelligent/smart draws from a distribution

(Morokoff and Caflish 1995). Random draws are commonly adopted usingpseudo-random sequences for the discrete points in the distribution. Bhat

(2001) showed that the coverage of random utility space is more

representative by a quasi-monte carlo approach that uses non-random and

more uniformly distributed sequences within the domain of integration. This

procedure, known as Halton sequences, offers the potential to reduce the


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number of draws that are needed for estimation of RPL models, thereby

reducing run times and/or reducing the simulation error that is associated

with a given number of draws. The number of draws is also another

important aspect and required to secure a stable set of parameter

estimates. In general, it appears that as the model specification becomes

more complex in terms of the number of random parameters and the

treatment of preference heterogeneity around the mean, the number of

required draws increases. There is no standard number but experience

suggests that a choice model with three alternatives and one or two random

parameters (no preference heterogeneity) can produce stability with as low

as 25 intelligent draws (i.e. Halton sequences, Bhat 2001; Train 2003),

although 100 appears to be a good number (Hensher and Greene 2003).

The best test however is to estimate models over a range of draws (say, 25,

50, 100, 250, 500, 1000 and 2000) for confirmation of stability/precision.

This kind of study is particularly important when deriving empirical

distributions for WTP indicators. Train (1999) and Bhat (2001) showed that

the simulation variance in the estimated parameters was lower using 100

Halton numbers than 1,000 random draws. With 125 Halton draws, they

found the simulation error to be half as large as with 1,000 random draws.

Hensher (2000) investigated Halton sequences involving draws of 10, 25,

50, 100, 150 and 200 (with three random generic parameters) and

compared the findings in the context of value of travel time savings (VTTS)

with random draws. In all the models investigated, Hensher concluded that

a small number of draws (as low as 25) produces model fits and mean VTTS

that are almost indistinguishable. This is a phenomenal development in the

estimation of complex choice models.

2.2.4 Valuing of Attributes

In discrete choice models, valuing of an attribute measure is relatively

straightforward as it is given by the ratio of partial derivatives of the

constant utility function with respect to that attribute and travel cost (i.e.

marginal rate of substitution between the attribute and travel cost at

constant utility).


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Suppose, the deterministic part V, of the utility function in the model

contains travel-time TT and travel-cost attribute TC. Then value of travel-

time (VOT) is simply computed as under.

/

/

V TT

VOT V TC

= (2.1)

With the commonly used linear-in-variables utility function, the above

formula reduces to /TT TC , where TT and TC are the coefficients of travel

time and travel cost respectively. It is important to appreciate that the

justification for this approach rests on a substantial body of microeconomic

theory that addresses the issue of how individuals allocate time and its

variation amongst alternatives.

Valuing of an attribute from MNL, NL and CHNL model estimates is done by

the above said marginal rate of substitution at constant utility. On the other

hand, valuing of an attribute from RPL model estimate is not straight

forward but depends to a great extent on the assumption of the random

distribution of an attribute. A judicial decision is required on the assumption

of random parameters to get an acceptable (say positive distribution for

time attribute) distribution and the value (i.e. estimating coefficients with

plausible sign) an attribute. If the mean estimate of random parameter

needs to be of specific sign (i.e. non-negative), then lognormal distribution

is favored. But the disadvantage of this distribution lies in its long upper tail

that is behaviorally implausible for valuation (Hensher 2000) especially in

estimating standard deviation. A uniform distribution with a (0, 1) bound is

sensible, when dummy variables are to be estimated. But if either of

normal, uniform, triangular distribution is used, then the disadvantage lies

with the wrong sign to some shares with the estimated value due to the

spread/standard deviation of the distribution. This disadvantage can be

avoided by imposing a constraint on the distribution by making

spread/standard deviation a function of the mean (Hensher and Greene

2001). Investigation on a constrained distribution has become an ongoing

line of research (Hensher and Greene 2003; Greene et al. 2006; Hensher

2006; Basu and Maitra 2007; Phanikumar and Maitra 2007) for estimating a


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plausible value of an attribute. In RPL, valuing of an attribute is constructed

using either the unconditional parameter estimate or the common-choice-

specific conditional parameter estimate (Hensher et al. 2005). In

unconditional parameter estimate, population moments are used to obtain a

distribution of the parameter estimate. Here each sampled individual is

randomly assigned along the continuous distribution. On the other hand, if

the individuals are assigned based on subjective priors, i.e. knowledge of

the chosen alternative, then valuing of attribute becomes conditional. Such

an approach (popular in Bayesian paradigms) enables the analyst to identify

common-choice-specific parameter estimate. In this case, there is a

possibility to observe that given a sample size, the frequency distributions

do not resemble the priori continuous distributions for the random

parameters (Sillano and Ortuzar 2005). On the other hand, unconditional

parameter estimates simply refer to the random distributions assumed for

RPL model development. Therefore, the present work considers only

unconditional parameter estimates as the scope of the investigation.

Accordingly, the RPL coefficients are estimated over the population using

classical estimation procedure.

The value of travel time measure from an unconditional parameter estimate

may be constructed as given from Equation 2.2 to 2.4. This way it is

possible to calculate value considering point estimates of coefficients but

this approach ignores sampling variance in these point estimates (as

correlation between random parameters are not considered).

Case 1:

When travel cost is fixed parameter with a coefficient estimateTC

and

travel-time is random parameter with a mean coefficient estimate TT

and

standard deviation/spread TTS = + ( ) /

TT TT r TCVOT S D (2.2)

Case 2:

When travel cost is random parameter with a mean coefficient estimateTC

and standard deviation/spreadTC

S , and also travel-time is random


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parameter with a mean coefficient estimate TT

and standard

deviation/spreadTT

S

= + + ( ) /( )TT TT r TC TC r

VOT S D S D (2.3)

Case 3:

When travel cost is fixed parameter with a coefficient estimateTC

and

travel-time is random parameter with a mean coefficient estimate TT

,

standard deviation/spreadTT

S and also heterogeneity coefficient estimate

( )TD H around the mean estimate of travel time for a heterogeneity study

against z observed variable (z can be defined as a dummy variable

interacting with travel time attribute).

= + + ( )( ) /TT TT H TT r TCVOT z S D (2.4)

Dr in all the above cases is a draw from an assumed distribution of random

parameter travel time. For example in triangular distribution, the draw is

obtained from a standard uniform distribution = [0,1]V U by

= < = 2 1 0.5 1 2(1 )r rD V if V else D V . In case of triangular

distribution, VOT can be calculated either by spread (like Case 1) or by

standard deviation formulae. For a triangular distribution, the standard

deviation is measured as / 6spread . So, VOT can then be measuredas( ( 6) ) /TT TT r TCS D + . For a triangular or constrained triangular

distribution (where say the mean equals the spread), then the distribution

of VOT for case 1/case 3 will be another triangular and/or constrained

triangular distribution.

2.2.5 Comparison o f Utility Equations

Selection of the utility equations obtained from different models is a key

issue. In the present work, selection of model is done considering goodness

of fit (2), t-statistics, log-likelihood and the rationality of modeling

technique. The utility equation selected in this manner from different

model specifications, is used for further analysis.


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2.2 .5 .1 The L ike l ihood Rat io Test

The most common test undertaken to compare any two models is the

likelihood ratio (LR) test. The LR statistics is given as:

1 2LR = -2 (LL - LL ) (2.5)

Where, LL1 and LL2 are the log likelihood at convergence for model 1 and

model 2 using same data set. The statistic used is chi-squared distributed

with (K2-K1) degrees of freedom, where K is the number of estimated

parameters. If the value of the LL-test exceeds the critical chi-squared value

then one can conclude that the two models are statistically different,

rejecting the null hypothesis of no difference. It may be noted that it is only

possible to compare log-likelihood estimates for models that share common

distributional assumption, e.g. the MNL model versus the more general RPL

model.

2.2.5 .2 Goodn ess of Fi t

One of the most well-known measures of goodness of fit is the pseudo R2. It

is defined as follows:

= 2(1)

1(0)

LL

LL(2.6)

The nominator is the value of the log likelihood function at the estimated

parameters and the denominator is its value when all the parameters are

set equal to zero. The larger the difference between the two log likelihood

values, the more the extended model adds to the very restrictive model.

Ben-Akiva and Lerman (1985), however, point out that interpreting rho

square whenever some additional independent variables are added is

problematic. They suggest the calculation of rho squared bar (adjusted rho

square)

Adjusted

= 2 (1)1

(0)

LL K

LL

Where, K is the number of parameters


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2.2.6 Selection of Models for Estimation of Ridersh ip

Utility equations developed using only SP data are acceptable for

estimation of WTP values. The attributes and variables contained within RP

data set are likely to be ill conditioned (e.g. largely invariant), and

therefore, parameter estimates (other than the alternative-specific

constant terms, i.e. ASCs) obtained from model using RP data are likely to

be biased. On the other hand, the attributes of SP data sets are likely to be

of good condition and hence, the associated parameter estimates obtained

from model using such data are likely to be unbiased. Nevertheless, the

ASCs estimated from SP data are likely to be behaviorally meaningless,

while those obtained from RP data sources likely to be of substantive

behavioral value (Hensher et al. 2005). The ASCs obtained from a discrete

choice models represent not only the average unobserved effect for each

alternative but also reflect the choice shares within the data set from which

it was estimated. For SP data, the choice shares will be obtained over a

number of hypothetical choice sets derived from some underlying

experimental design, each of which is given to multiple individuals. Beyond

representing the average unobserved effects, the ASCs obtained from SP

data may be meaningless (particularly for studies involving demand

forecasting). On the other hand ASCs, acquired from RP data with or

without other attributes should reflect the true choice shares observed over

the population. Louviere et al. (2000) also argued on the same line that

analyst should exploit strengths of both data sources while discarding the

weakness displayed b

thesis ssd defence

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