ON THE RELATIONSHIP OF INFORMATION AND (TELE)COMMUNICATION SYSTEMS WITH ACTIVITY  PARTICIPATION AND TRAVEL

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ON THE RELATIONSHIP OF INFORMATION AND (TELE)COMMUNICATION SYSTEMS WITH ACTIVITY  PARTICIPATION AND TRAVEL

ABSTRACT

During the last decade we have experienced the rapid advance and growing popularity of information and communications technology (ICT).  It began to alter the way in which people conduct their everyday affairs and also the way in which businesses are conducted.  Under these circumstances, our traditional concept of accessibility can no longer be valid. In fact, through ICT people can get virtual accessibility to a rapidly growing range of activities without the more traditional spatial and temporal limitations and constraints.   Consequently, people have more flexibility to arrange their schedules, and eventually change their activity and travel patterns.   These substantial impacts of ICT motivate the need for research on the present and future impacts of telecommunication on activity and travel behavior.

 

With the Puget Sound Transportation Panel (PSTP) data, especially the data collected in Wave7 (1997) and Wave 9 (2000), this study attempts to examine a variety of aspects of the relationships between ICT and activity and travel behaviors within comprehensive conceptual model systems examining correlation patterns.   Several models have been developed, each of which focuses more on a specific aspect of the relationships.

 

First, technology choice models were developed to identify user groups for each ICT device. Multivariate multilevel categorical data models were used to account for a strong behavioral correlation among households and within household members as well as a high degree of people heterogeneity in technology adoption.  The model revealed that decisions of ICT ownership and usage are most likely determined by joint decisions among members of the same household and there is heterogeneity in each type of ICT ownership and use among different situations.

 

Second, a comparative analysis of three different model systems, including a set of singleequation regression models, seeming unrelated regression models (SUR Model), and multivariate multilevel models, was conducted to examine the effect of ICT on activity and travel duration.  Although the three different models produce very similar coefficient values, multivariate models are more advantageous over single-equation models.  The multivariate models account for the correlation of the error terms across equations, and as a result, they produce smaller standard

 

errors of the coefficient estimates than those from single-equation models.  Between the two multivariate models, the multilevel models are superior because they consider the hierarchy of level in the data and provide more information by estimating variance-covariance matrices and correlations for each of the multiple levels, offering additional insight on behavioral heterogeneity at each level.

 

Third, the joint models using a structural equation modeling technique are formulated for daily time allocation to various activities (subsistence, maintenance, and leisure) and travel, and mode frequency (driving alone, shared ride, transit, bike, walk, and others) as a function of crosssectional and longitudinal information on personal and household socio-demographics and telecommunication technology ownership and usage.  In this way, the impacts of information and communication technologies on daily time allocation to various activities and travel, and on modal split are assessed. At the same time, the complex relationships among different activities and travel time use indicators and their daily frequencies are explored.  In addition, using longitudinal information on ICT, it is possible to check whether or not the changes in ICT ownership and usage have symmetric effects on time use for activity and travel behavior.   From this model, it was found that the “technology” effect depends on the location and type of technology and that the majority of social, economic, and ICT changes have asymmetric effects on behavior.

 

Fourth, dynamic analysis of time use and frequency of activity and travel between Wave 7 and Wave 9 is conducted using latent class clustering and structural equation modeling.  A fixed time budget is explicitly taken in account in these models, and existence of very strong substitutional and complementary relationships in time use among different activities and travel are examined.  In addition, as a new approach to account for state dependence (past activity and travel behavior effects) in the model, relatively homogenous daily activity and travel behavior patterns in Wave 7 are first identified through latent class cluster analysis.  Then time use for a specific activity (in-home activity, out-of-home subsistence activity, and out-of home non-subsistence activity) and traveling, and frequency of episodes by activity in Wave 9 are modeled as a function of cross-sectional and longitudinal information as well as activity and travel patterns in Wave 7.  The model results showed that out-of-home activity duration and travel time have very large substitutional effects on in-home activity duration, while out-of-home activity duration has complementary effects on travel time.   The model results also confirmed the existence of strong habit persistence in activity engagement and time use even in a relatively long period of time.

TABLE OF CONTENTS

 

LIST OF TABLES…………………………………………………………………………………………………………. viii

LIST OF FIGURES…………………………………………………………………………………………………………. ix

ACKNOWLEDGMENTS…………………………………………………………………………………………………. x

 

 

CHAPTER 1 INTRODUCTION………………………………………………………………………………………..1

1.1 Research Objectives……………………………………………………………………………………………….. 1

1.2 Organization of Thesis……………………………………………………………………………………………. 3

 

CHAPTER 2 LITERATURE REVIEW………………………………………………………………………………5

2.1 Different Impacts of ICT on Transportation………………………………………………………………. 5

2.2 The Impact of Telecommunication on the Demand for Transportation …………………………. 7

2.2.1 Substitution…………………………………………………………………………………………………….. 8

2.2.2 Stimulation……………………………………………………………………………………………………. 11

2.3 The Impact of Telecommunication on the Supply of Transportation…………………………… 12

2.4 Telecommunication and Time Allocation for Activity and Travel ……………………………… 13

2.5 Panel Analysis……………………………………………………………………………………………………… 15

 

CHAPTER 3 PUGET SOUND TRANSPORTATION PANEL……………………………………………18

3.1 Wave 7 and Wave 9……………………………………………………………………………………………… 24

 

CHAPTER 4 ANALYSIS METHODS……………………………………………………………………………..26

4.1 Linear Regression Model………………………………………………………………………………………. 26

4.2 Seemingly Unrelated Regression Model (SUR Model)……………………………………………… 27

4.3 Multilevel Model …………………………………………………………………………………………………. 29

4.4 Latent Class (LC) Cluster Analysis ………………………………………………………………………… 32

4.5 Structural Equation Model (SEM)………………………………………………………………………….. 35

 

CHAPTER 5 A MULTIVARIATE MULTILEVEL ANALYSIS OF TECHNOLOGY CHOICE41

5.1 Introduction…………………………………………………………………………………………………………. 41 5.2 Data Description ………………………………………………………………………………………………….. 41 5.3 Model Formulation ………………………………………………………………………………………………. 42

5.4 Model Results ……………………………………………………………………………………………………… 45

5.4.1 Cross-sectional Effects……………………………………………………………………………………. 47

5.4.2 Longitudinal Effects……………………………………………………………………………………….. 49

5.5 Summary…………………………………………………………………………………………………………….. 51

 

CHAPTER 6 COMPARATIVE ANALYSIS OF THREE DIFFERENT ESTIMATION

METHODS……………………………………………………………………………………………………………………52

6.1 Introduction…………………………………………………………………………………………………………. 52

6.2 Data Used……………………………………………………………………………………………………………. 52

6.3 Model Formulation ………………………………………………………………………………………………. 55

6.3.1 Single-Equation Regression Model ………………………………………………………………….. 55

6.3.2 Seemingly Unrelated Regression Model (SUR Model)……………………………………….. 56

6.3.3 Multivariate Multilevel Model…………………………………………………………………………. 57

6.4 Model Results ……………………………………………………………………………………………………… 58

6.4.1 Single-Equation Model Results ……………………………………………………………………….. 59

6.4.2 Seemingly Unrelated Regression (SUR) Model Results……………………………………… 61

6.4.3 Multivariate Multilevel Model Results……………………………………………………………… 63

6.5 Summary…………………………………………………………………………………………………………….. 67

 

CHAPTER 7 CROSS-SECTIONAL AND LOGITUDINAL RELATIONSHIPS AMONG INFORMATION AND TELECOMMUNICATION TECHNOLOGIES, DAILY TIME

ALLOCATION TO ACTIVITY AND TRAVEL, AND MODAL SPLITS……………………………69

7.1 Introduction…………………………………………………………………………………………………………. 69

7.2 Data Used……………………………………………………………………………………………………………. 71

7.3 Model Formulations……………………………………………………………………………………………… 74

7.4 Model Results ……………………………………………………………………………………………………… 76

7.4.1 Cross-sectional Effects……………………………………………………………………………………. 78

7.4.2 Longitudinal Effects……………………………………………………………………………………….. 83

7.4.3 Longitudinal Effects of ICT…………………………………………………………………………….. 87

7.5 Implications of ICT Changes…………………………………………………………………………………. 91

7.6 Summary…………………………………………………………………………………………………………….. 95

 

CHAPTER 8 DYNAMIC ANALYSIS OF TIME USE AND FREQUENCY OF ACTIVITY

AND TRAVEL WHILE ACCOUNTING FOR HISTROTY DEPENDENCY……………………….97

8.1 Introduction…………………………………………………………………………………………………………. 97

8.2 Data Used………………………………………………………………………………………………………….. 100

8.3 Model Formulations……………………………………………………………………………………………. 102

8.4 Model Results ……………………………………………………………………………………………………. 104

8.4.1 Activity Participation Patterns in 1997……………………………………………………………. 104

8.4.2 Cross-sectional Effects………………………………………………………………………………….. 107

8.4.3 Longitudinal Effects……………………………………………………………………………………… 115

8.4.4 Longitudinal Effects of ICT…………………………………………………………………………… 117

8.4.5 State Dependence Effects ……………………………………………………………………………… 120

8.5 Summary…………………………………………………………………………………………………………… 121

 

CHAPTER 9 CONCLUSIONS AND FUTURE WORK……………………………………………………123

9.1 Summary and Conclusions ………………………………………………………………………………….. 123

9.2 Future Research …………………………………………………………………………………………………. 126

 

REFERENCES …………………………………………………………………………………………………………….128

 

APPENDIX: List of Explanatory Variables……………………………………………………………………..135

CHAPTER 1                                                                              INTRODUCTION

 

The 1990s’ explosive growth and continued proliferation in information and communications technology (ICT) (i.e., widespread use of computers, mobile phone, e-mail, the Internet, and ecommerce) began to alter the way in which people conduct their everyday affairs and also the way in which business is conducted.  Under these circumstances, accessibility can no longer be measured only in terms of travel time, distance, or generalized travel costs (Golob, 2001; Golob and Regan, 2001).  In fact, information technology gives individuals virtual accessibility to a rapidly growing range of activities without the more traditional spatial and temporal limitations and constraints.   For example, ICT makes it possible for people to work at any place (telecommute) and to buy goods without a physical trip to the store (teleshopping or ecommerce).  It therefore allows people more flexibility to arrange their schedules, and eventually changes their activity and travel patterns. In addition, access to technologies and knowledge about ICT are not uniform across the population.  These substantial and differential impacts of ICT create the need for research on the present and future impacts of telecommunication on activity and travel behavior.

 

To date, a substantial amount of research on the relationship between ICT and transportation has been conducted, but most studies focused on the potential impacts of ICT on mobility, especially the degree of substitution and complementarity between transportation and telecommunication. In addition, many hypotheses have been empirically tested mostly in the telecommuting arena using data from one time point.  There is still a gap in the literature on the effects of change in ICT availability and use and their effect on travel behavior, and very few studies exist to date on other broader aspects of ICT impacts on activity and travel behavior.

 

1.1  Research Objectives

The objectives of this research are to investigate relationships between ICT ownership and use and people’s activity and travel behaviors from a variety of viewpoints using survey data instead of conceptual models and theoretical frameworks.  Specifically using a variety of analytical methods the following hypotheses are tested:

 

Examine if trends for each type of ICT ownership and use over time exist;

Examine if there is heterogeneity in each type of ICT ownership and use among different situations in terms of age group, income, or occupation;

Examine if there is a substitution or complementarity relationship between ICT and travel;

Examine if the effects of ICT ownership and use on activity and travel behavior depend on the length of technology ownership and use; and

Examine if there is symmetry in activity and travel behavior when people gain and lose ICT.

 

All these hypotheses, taken together, will provide a significant advancement in our knowledge about the interaction between ICT and activity and travel behavior by individuals.  In all analyses a variety of other factors influencing the relationship between ICT, activity participation, and travel will also be controlled for.

 

To accomplish the research objectives, the Puget Sound Transportation Panel (PSTP) data are used, specifically the data collected in Wave 7 (1997) and Wave 9 (2000), within a more general and comprehensive conceptual model system shown in Figure 1.   Due to the complexity of the model system, it is impossible to incorporate all the components in a single model.  Therefore, several (sub)models have been developed, each of which focuses on a specific aspect of the relationships of Figure 1.

 

 

Figure 1-1 Comprehensive Conceptual Model

 

 

1.2  Organization of Thesis

 

Chapter 2 of this thesis is an overview of the past and current understanding on the relationships between telecommunication and transportation offered as background.   The background contains the effects of telecommunication on the demand for and the supply of transportation.

 

Chapter 3 provides a general description of the Puget Sound Transportation Panel (PSTP) data that are used in this study.

 

Chapter 4 describes the analytical methods used in this study including the linear regression models, Seemingly Unrelated Regression models, Multilevel Regression models, Latent Class Cluster analysis, and Structural Equations Modeling.

 

Chapter 5 explains the development of technology choice models to identify user groups for each ICT device. Multivariate multilevel categorical data models are used to account for a strong correlation within household members as well as a high degree of people heterogeneity in technology adoption.

 

Chapter 6 describes the comparative evaluations of three different models (single-equation regression models, seeming unrelated regression models, and multivariate multilevel models) for the analysis of the impact of various telecommunication technologies on time use for out-ofhome activity and travel.

 

Chapter 7 explores the complex inter-relationships among ICT ownership and use, time allocation to various activities and travel, and mode choice using structural equation modeling technique.

 

Chapter 8 provides a description of dynamic analysis of the impacts of ICT on time use and frequency of activity and travel using structural equation modeling technique. A new approach to take into account state dependence (past activity and travel behavior effects) is also demonstrated through Latent Class Cluster analysis.

 

Chapter 9 contains a summary of the research and a description of the need for further research to improve our current understanding of the relationships between ICT and activity and travel behavior.

 

ON THE RELATIONSHIP OF INFORMATION AND (TELE)COMMUNICATION SYSTEMS WITH ACTIVITY  PARTICIPATION AND TRAVEL

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