Modelling Strategic Behaviour in Anticipation of Congestion

173 Pages·2006·1.46 MB·English

Modelling Strategic Behaviour
in Anticipation of Congestion

PROEFSCHRIFT

ter verkrijging van de graad van doctor aan de
Technische Universiteit Eindhoven, op gezag van de
Rector Magnificus, prof.dr.ir. C.J. van Duijn, voor een
commissie aangewezen door het College voor
Promoties in het openbaar te verdedigen
op woensdag 22 maart 2006 om 16.00 uur

door

Qi Han

geboren te Nanjing, China
Dit proefschrift is goedgekeurd door de promotoren:

prof.dr. H.J.P. Timmermans
en
prof.dr. W.F. van Raaij

Copromotor:
prof.dr.ir. B.G.C. Dellaert

Copyright © 2006 Q. Han
Technische Universiteit Eindhoven,
Faculteit Bouwkunde, capaciteitsgroep Stedebouw

Cover design: Tekenstudio, Faculteit Bouwkunde
Printed by the Technology University of Eindhoven Press Facilities

CIP-DATA KONINKLIJKE BIBLIOTHEEK, DEN HAAG
ISBN 90-6814-598-3
Preface

In this thesis, a approach is developed to explore and describe strategic choice behaviour
in anticipation of congestion, based on interaction and interdependencies between
individuals and between individuals and service providers. It formulates the process of
how people would arrive at choices in a congested environment and how information or
guidance would become effective towards serving a certain goal. My research interest in
this area emerged during my master study. When I first saw the research proposal of this
Ph.D. project a few years ago, I knew that I found something that would suit me.
The research project was sponsored by SOBU: a collaboration between Tilburg
University and Eindhoven University of Technology. The original proposal was
developed by Harry Timmermans and Benedict Dellaert when he still was in Tilburg
University. Soon after the proposal was funded, he became a professor at Maastricht
University and Fred van Raaij joined the team. However, Benedict stayed heavily
involved in the project, and hence I was lucky to have such an active scholarly
environment. The available travel grant provided me also great opportunities to attend
international conferences and meet other researchers. Besides, many people have
supported me in conducting the research reported in this thesis. I wish to acknowledge all
the help that they given. There are some that I would like to mention specifically for their
contributions.
iii
Professor Harry Timmermans, my first supervisor, has supported me in many ways.
With his special style of open guidance he knows how to encourage people to work
independently, to develop their own expertise and to pursue their research goals. This and
his strategic insights in the world of science have helped me in not only developing the
line of my research interest, but also offering me a wonderful opportunity to learn how to
conduct research and publish in international journals. Besides, his kind and careful
comments on the draft version of my thesis were very important.
Many thanks also go to Professor Fred van Raaij, my second supervisor. He
succeeded Professor Benedict Dellaert and continued bridging the cooperation between
the two universities on this project. He introduced me the educational credit system in
Tilburg University that I could use to get sufficient respondents for the data collection.
From preparing the recruit announcement, arranging the laboratory, to credit registration
afterwards, he has given me invaluable support in two experimental data collections in
my research project. Besides, his insightful reviews gave valuable contributions to my
work.
I also owe much to Professor Benedict Dellaert, my co-promotor. He was my
second supervisor for the first half year before he took the job position at Maastricht
University. His contribution to my research however never stopped. His ability to find
creative solutions for complicated problems and his efforts to discuss my work at the
most detailed level are much appreciated. His suggestions and helpful comments on my
work have improved the quality of my research significantly.
During the years, my colleagues in the urban planning group supported me in many
different ways. I learned many things from working with them and I feel honoured simply
to be surrounded by them. Although it is difficult to put in words, I want to take this
opportunity to express my appreciation to all of them, in particular to Aloys Borgers,
Theo Arentze, Peter van der Waerden and Astrid Kemperman for helping me out in
explaining models and software, Leo van Veghel for providing me access to valuable
references, and both Mandy van de Sande – van Kasteren and Anja Janssen for their
excellent secretarial assistance and care. Also I enjoyed a lot the discussions with former
group members Manon van Middelkoop, Chang-Hyeon Joh, Maarten Ponje and current
Ph.D. candidates. Among them, a special word of thanks goes to Dick Saarloos, who
shared the office with me for the last few years, for the interesting talks on research and
cultural difference, and for his help in times of need.
Finally, I would like to thank my husband, Hai. His support has been a constant
encouragement. Last but not least, I also like to thank my parents, who have always been
there for me with their unconditional care.

Qi Han
Eindhoven, December 2005
iv
Contents

Preface iii
List of figures ix
List of tables x

1 Introduction 1

2 Modelling spatial choice behaviour in urban planning 5
2.1 Introduction 5
2.2 Individual choice models 6
2.2.1 Spatial interaction modelling 6
2.2.2 Discrete choice modelling 7
2.2.3 Activity-based modelling 9
2.2.4 Evaluation 9
2.3 Choice behaviour in response to information 10
2.3.1 Information use and provision 10
2.3.2 Evaluation 11
2.4 Choice behaviour in response to other individuals’ behaviour 12
2.4.1 Interdependence within multi-person households 12

(intra household)
2.4.2 Interdependence within the social-spatial network 12

(inter household)
2.4.3 Game theory and interactive decision making 14
2.4.4 Game theory and discrete choice models 15
2.4.5 Evaluation 15
2.5 Conclusion and discussion 16

3 Theory 17
3.1 Introduction 17
3.2 Models of strategic choice behaviour based on interaction and 18

interdependencies between individuals
v
3.2.1 N-player J-option game formulation 18
3.2.2 Strategic discrete choice model 22
3.2.2.1 Specification of the exogenous component 22
3.2.2.2 Specification of the endogenous component 23
3.2.2.3 Specification of the disturbance 24
3.2.3 Equilibrium conception: consistence condition 24
3.2.3.1 Bayesian Nash equilibrium 25
3.2.3.2 Quantal response equilibrium 27
3.2.3.3 Correspondence between QRE and BNE 28
3.2.4 The logit approach 30
3.2.4.1 The error parameter 31
3.2.4.2 Estimation issues 33
3.2.5 Inconsistence: imperfect expectation in one-shot situation 35
3.2.6 Interpretation, application 37
3.2.6.1 Understanding mixed-motives 37
3.2.6.2 Application issues: user, domain and data 37
3.3 Models of strategic choice behaviour based on interaction and 38

interdependencies between individuals and information providers
3.3.1 Two-player two-stage game formulation 39
3.3.2 Interactive decisions between individuals and information 41

providers
3.3.3 Update of individual’s expectation given information providers’ 42

recommendation
3.3.4 Information provider’s objective 44
3.4 Conclusion and discussion 46

4 Numerical simulation 49
4.1 Introduction 49
4.2 Interactive decisions between individuals 50
4.2.1 Algorithm 51
4.2.2 Simulation results 51
4.2.2.1 Sensitivity to choice alternative facets 52
4.2.2.2 Sensitivity to interaction 53
4.2.2.3 Sensitivity to uncertainty of conjectures 54
4.3 Interactive decisions between individuals and an information 55

provider
vi
4.3.1 Algorithm 56
4.3.2 Simulation results 57
4.3.2.1 Effects of different objectives of the information 58

provider
4.3.2.2 Effects of different levels of individuals’ conjecture 60

ability
4.4 Conclusion and discussion 63

5 Timing choice 65
5.1 Introduction 65
5.2 Hypothesis 66
5.3 Timing choice data 67
5.3.1 Data collection 67
5.3.2 Dinner timing choice 68
5.3.3 Participation timing choice 69
5.3.4 Structure of the experimental design 70
5.4 Analysis of timing choice data 71
5.4.1 Dinner timing choice model 71
5.4.2 Participation timing choice model 77
5.4.3 Non-strategic and strategic behaviour 79
5.4.4 Test of hypothesis 84
5.5 Conclusion and discussion 88

6 Repeated strategic choice 89
6.1 Introduction 89
6.2 Research question 90
6.3 Repeated strategic choice data 92
6.3.1 Data collection 92
6.3.2 Structure of the experimental design 93
6.3.3 Implementation 94
6.3.4 Process 95
6.4 Analysis of repeated strategic choice data 98
6.4.1 General results: performance 98
6.4.2 General results: choice frequencies 101
6.4.3 Regression model estimation 103
vii
6.4.4 Choice model estimation 106
6.4.4.1 Strategic choice model 106
6.4.4.2 Simulation for projecting the anticipated congestion 108
6.4.4.3 Results for one-shot situations 108
6.4.4.4 Results for repeated situations 111
6.4.4.5 Results about learning in situations without 113

recommendation
6.4.4.6 Results about learning in situations with 115

recommendation
6.5 Conclusion and discussion 118

7 Conclusion and discussion 121
7.1 Introduction 121
7.2 Short summary of the study 122
7.3 Discussion of the proposed modelling approach 124
7.4 Avenues for future research 125

Bibliography 129

Appendix A-1 Announcement of timing choice experiment 137
Appendix A-2 Questionnaire of timing choice experiment 138
Appendix B-1 Announcement of repeated choice experiment 147
Appendix B-2 Overview of the tasks 148
Appendix B-3 Screen shot of the instruction 149
Appendix B-4 Screen shot of the tips 150
Appendix B-5 A screen shot after subjects made a choice 151

Author index 153
Subject index 157
Samenvatting (Dutch summary) 161
Curriculum vitae 163

viii
List of figures

Figure 3-1 Game 1 formulation 20
Figure 3-2 Game 1 extensive form 21
Figure 3-3 Relation between type and strategy 29
Figure 3-4 Error parameter effect 32
Figure 3-5 Game 2 formulation 40
Figure 3-6 Game 2 extensive form 40
Figure 3-7 Effect of self-expectation on compliance rate 44
Figure 4-1 Choice alternative impact 53
Figure 4-2 Interaction impact (θ) 54
Figure 4-3 Uncertainty impact 55
Figure 4-4 Results for the Nash equilibrium 58
Figure 4-5 Results for the quantal response equilibrium 59
Figure 4-6 Results for the noisy introspection model 60
Figure 4-7 Results for the equal usage objective 61
Figure 4-8 Results for the welfare maximum objective 61
Figure 4-9 Results for the user equity objective 62
Figure 5-1 Structure of timing choice experimental design 71
Figure 5-2 Simulations of participation timing choice 86
Figure 6-1 Structure of two dimensional effects 91
Figure 6-2 A screen shot of choice task 97
Figure 6-3 Average performance with and without recommendation 98
Figure 6-4 Average performance with recommendation for different 99
objectives
Figure 6-5 Average performances for the certainty of others’ preference 100
Figure 6-6 Recommendation effect in choice frequencies 101
Figure 6-7 Preference certainty effect in choice frequencies 102
Figure 6-8 Learning effect in the no recommendation situation 114
Figure 6-9 Learning effect in the with recommendation situation 115
Figure 6-10 Learning effect for the marginal disutility of congestion 117
Figure 6-11 Learning effect for the certainty of others’ preference 118

ix
List of tables

Table 5-1 Overview of registration for timing choice experiment 67
Table 5-2 Non-strategic dinner timing choice estimates (1-A) 72
Table 5-3 Strategic dinner timing choice estimates (1-B) 74
Table 5-4 Dinner timing choice model estimates (1-A & 1-B) 76
Table 5-5 Participation timing choice estimates (2-B & 2-C) 78
Table 5-6 Observed participation timing choice frequencies 78
Table 5-7 Non-strategic behaviour estimates (1-A & 2-B) 79
Table 5-8 Non-strategic behaviour contrast parameter estimates (1-A & 2-B) 80
Table 5-9 Strategy behaviour estimates (1-B & 2-C) 81
Table 5-10 Strategy behaviour contrast parameter estimates (1-B & 2-C) 82
Table 5-11 Information impacts (2-C) 83
Table 5-12 Simulations of participation timing choice 86
Table 6-1 Overview of registration for repeated strategic choice 92
Table 6-2a Regression model estimates (1st round vs. 13th round) 104
Table 6-2b Test statistics for the classical regression model 104
Table 6-3 Contrast parameter estimates (1st round vs. 13th round) 105
Table 6-4 One-shot situation strategic choice model estimates (1st round) 109
Table 6-5 One-shot situation MNL model log likelihoods (1st round data) 110
Table 6-6 Repeated situation strategic choice model estimates (13th round) 112
Table 6-7 Repeated situation MNL model log likelihoods (13th round data) 113

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