Modeling the influence of structural lifecycle events on activity-travel decisions using a structure learning algorithm

This paper describes the results of a study on the impact of lifecycle events on activity-travel choice decisions of individuals. An Internet-based survey was designed to collect data concerning structural lifecycle events. In addition, respondents answered questions about personal and household characteristics, possession and availability of transport modes and their current travel behavior. In total, 710 respondents completed the online survey. The complexity of transport mode choice is modeled using a Bayesian Belief Network. Previous papers describe the conceptual framework underlying the model and the temporal effects of lifecycle events on mode choice. This paper focuses on influences of structural life trajectory events on each other and on changes in resources that impact activity-travel decisions. We investigate the extent to which causal relations exist between these events and their direct and indirect effects on changes in transport mode availability and the possession of transit passes. A structure learning algorithm is used to build a Bayesian Belief Network of interdependencies between these events from the data.

[1]  Ta Theo Arentze,et al.  Modeling the Impact of Key Events on Long-Term Transport Mode Choice Decisions: Decision Network Approach Using Event History Data , 2005 .

[2]  K. Train.  Discrete Choice Methods with Simulation , 2003 .

[3]  Ta Theo Arentze,et al.  Modeling learning and adaptation processes in activity-travel choice A framework and numerical experiment , 2003 .

[4]  Hjp Harry Timmermans,et al.  Key events and critical incidents influencing transport mode switching behavior - an exploratory study , 2003 .

[5]  Harry Timmermans,et al.  Examining Temporal Effects of Lifecycle Events on Transport Mode Choice Decisions , 2007 .

[6]  P. Spirtes,et al.  Causation, prediction, and search , 1993 .

[7]  Anders L. Madsen,et al.  Hugin - The Tool for Bayesian Networks and Influence Diagrams , 2002, Probabilistic Graphical Models.

[8]  A. P. Dawid,et al.  Fast retraction of evidence in a probabilistic expert system , 1992 .

[9]  Kay W. Axhausen,et al.  A Dynamic Understanding of Travel Demand: A Sketch , 2005 .

[10]  Martin Lanzendorf,et al.  Impact of Life-Course Events on Car Ownership , 2006 .

[11]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[12]  Harry Timmermans,et al.  THE INFLUENCE OF KEY EVENTS AND CRITICAL INCIDENTS ON TRANSPORT MODE CHOICE SWITCHING BEHAVIOUR: A DESCRIPTIVE ANALYSIS , 2003 .

[13]  Michael I. Jordan.  Learning in Graphical Models , 1999, NATO ASI Series.

[14]  Jordan J. Louviere,et al.  Analyzing Decision Making: Metric Conjoint Analysis , 1988 .

[15]  I. Ajzen,et al.  Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research , 1977 .

[16]  David J. Spiegelhalter,et al.  Probabilistic Networks and Expert Systems , 1999, Information Science and Statistics.

[17]  S. Lauritzen.  The EM algorithm for graphical association models with missing data , 1995 .

[18]  Nils Fearnley,et al.  Summary: Short- and Long-run Demand Effects in Transport: a Literature Survey , 2005 .

[19]  Moshe Ben-Akiva,et al.  Discrete Choice Analysis: Theory and Application to Travel Demand , 1985 .

[20]  Haiyuan Wang,et al.  Modeling Travel Mode Choice Behavior by Bayesian Network , 2006 .