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176 Pages·2013·1.2 MB·English

Temporal dependence in life
trajectories and mobility decisions
ISBN 978-90-393-6051-4

Copyright © Abu Toasin Md Oakil, Faculty of Geosciences, Utrecht University 2013.

All rights reserved. No part of this publication may be reproduced, stored in a retrieval
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recording, or otherwise without permission in writing from the publisher.
Temporal dependence in life trajectories and
mobility decisions

De tijdafhankelijkheid van levensgebeurtenissen en mobiliteitskeuzen
(met een samenvatting in het Nederlands)

Proefschrift

ter verkrijging van de graad van doctor aan de Universiteit Utrecht op gezag van de rector
magnificus, prof.dr. G.J. van der Zwaan, ingevolge het besluit van het college voor promoties
in het openbaar te verdedigen

op maandag 28 oktober 2013 des middags te 2.30 uur

door

Abu Toasin Md Oakil

geboren op 22 november 1980 te Rajshahi, Bangladesh
Promotoren: Prof. dr. P. Hooimeijer
Prof. dr. H.J.P. Timmermans

Co-promotoren: Dr. D.F. Ettema
Dr. T.A. Arentze

This thesis was accomplished with financial support from De Nederlandse Organisatie voor
Wetenschappelijk Onderzoek (NWO)
Contents

Contents 3
Figures 5
Tables 7
Acknowledgements 9

Chapter 1: Introduction 11
1.1 Background 11
1.2 Objectives of the study 13
1.4 Scope of the study 13
1.5 Organisation of the study 14

Chapter 2: Conceptual framework 17
2.1 Introduction 17
2.2 Conceptual Framework 19
2.3 Methodology: Bayesian belief network 22
2.4 Data description 25
2.5 Analysis and results 26
2.6 Conclusion 32

Chapter 3: Survey design 35
3.1 Introduction 35
3.2 Survey method 36
3.3 Features of the questionnaire 37
3.4 Sampling procedure 44
3.5 Sample description 45
3.6 Household and mobility events 47
3.7 Social network 49
3.8 Market perception 49
3.9 Conclusion 49

Chapter 4: Changing car ownership level: Household’s adaption and anticipation 53
4.1 Introduction 53
4.2 Literature review 54
4.3 Approach 56
4.4 Data and method 58
4.5 Results 62
4.6 Discussion 67
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Chapter 5: Residential relocation: An analysis of history dependence 69
5.1 Introduction 69
5.2 Literature review 70
5.3 Data description and method 72
5.4 Results 77
5.5 Discussion 81

Chapter 6: Car commuting decision: An analysis of commute mode changing decision 83
6.1 Introduction 83
6.2 Research design 85
6.3 Data analysis 86
6.4 Results 87
6.5 Conclusion 91

Chapter 7: Bicycle commuting decision: An analysis of commute mode changing
decision 93
7.1 Introduction 93
7.2 Analytical approach 96
7.3 Data 97
7.4 Results 102
7.5 Conclusion 105

Chapter 8: Residential, employment and car ownership decisions - An integrated
model of dynamics 107
8.1 Introduction 107
8.2 Methodology 110
8.3 Data collection 112
8.4 Results 116
8.5 Conclusion 127

Chapter 9: Summary and discussion 129
9.1 Summary 129
9.2 Discussion 131
9.3 Direction for future research 132

References 135
Appendices 145
Samenvatting 169
Curriculum vitae 173

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Figures

Figure 1.1 : Organisation of the thesis.
Figure 2.1 : Conceptual framework of long-term mobility decisions
Figure 2.2 : Conceptual framework of mobility decisions (cross-sectional)
Figure 2.3 : Network learning – relationship between events, states and decisions.
Map 3.1 : Survey area in the geographical context of Utrecht province.
Figure 4.1 : A sample showing the design of the questionnaire
Figure 8.1 : Analytical framework of data analysis
Figure 8.2 : Integrated model framework
Figure 8.3 : Structure of causal relationship among mobility decisions, household situation
and events.
Figure 8.4 : Results of parameter learning in BBN
Figure 8.5 : Without any evidence from influential variables
Figure 8.6 : With the evidence for households living in apartments
Figure 8.7 : With joint evidence for households living in apartments and starting
cohabitation
Figure 8.8 : With joint evidence for households living in apartments, starting cohabitation
and expecting the first child next year

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Tables

Table 2.1 : State dependency of apartment living and impact of income and age on the
relocation decision and dwelling type preference
Table 2.2 : State dependency of terraced housing and impact of income and age on the
relocation decision and dwelling type preference
Table 2.3 : Satisfaction level and probability of moving for different age and income
categories
Table 3.1 : A sample format of the first section of the questionnaire.
Table 3.2 : A sample questionnaire asking about household status
Table 3.3 : A sample questionnaire asking about residential and work location
Table 3.4 : A sample questionnaire about a household’s transport options and travel.
Table 3.5 : A sample of the prospective section of the questionnaire.
Table 3.6 : A sample of how social network information was collected.
Table 3.7 : Geographical distribution of the respondents over the survey areas
Table 3.8 : Highest education achieved by the respondent
Table 3.9 : Distribution of the income of respondents for 2010
Table 3.10 : History of household events
Table 3.11 : History of mobility events
Table 3.12 : Intended events
Table 3.13 : Residential, work and travel situation of peers from the respondents’ social
network
Table 3.14 : Market perception of the respondent
Table 4.1 : Descriptive statistics of event history
Table 4.2 : Results of panel data analysis of car ownership change
Table 5.1 : Event occurrence in the sample
Table 5.2 : Cross tabulation of residential relocation by household and other mobility events
Table 5.3 : Result of mixed logit analysis for residential relocation in a particular calendar
year
Table 6.1 : Results of the bivariate analysis
Table 6.2 : Results of the panel analysis
Table 7.1 : Descriptive statistics of socio-demographic and other state variables in two
samples
Table 7.2 : Modal shift occurrence
Table 7.3 : Descriptive statistics about life-cycle and mobility events
Table 7.4 : Result of modal shifts from and to bicycle commuting
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Table 8.1 : Sample distribution over socio-economic characteristics and mobility states of
the household
Table 8.2 : Occurrence of household and mobility events
Table 8.3 : The prediction (%) of moving up with evidences of dwelling type and household
events (CURRENT year)
Table 8.4 : The prediction (%) of moving up with evidences of dwelling type and
cohabitation (NEXT year)
Table 8.5 : The prediction (%) of car acquisition with evidences of household car ownership
and household events (CURRENT year)
Table 8.6 : The prediction (%) of car acquisition with evidences of household car ownership
and cohabitation (NEXT year)
Table 8.7 : The prediction (%) of employer change with evidences of household working
status, cohabitation and age of the respondent (CURRENT year)
Table 8.8 : The prediction (%) of employer change with evidences of household working
status, cohabitation and age of the respondent (NEXT year)

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