Individuals' Activity–Travel Rescheduling Behaviour: Experiment and Model-Based Analysis

Rescheduling of daily activities and associated travel in response to unforeseen events such as travel delays is receiving increased attention in the context of traffic management. In this paper we describe the results of a stated adaptation experiment held among a large sample of individuals through a web-based questionnaire, to estimate parameters of such dynamic behaviour. In the experiment subjects indicated their response to a reduction in available time for a planned activity in a number of hypothetical situations. A mixed logit model was used to estimate subjective preferences for adapting in certain ways conditional upon activity attributes and socioeconomic variables. The results indicate that location and transport-mode adaptations are rare compared with duration adjustment or postponing (or cancelling) the activity dependent on the relative size of reduced time. Socioeconomic variables and activity attributes also play a significant role.

[1]  Ta Theo Arentze,et al.  Activity-Travel Scheduling and Rescheduling Decision Processes: Empirical Estimation of Aurora Model , 2004 .

[2]  Harry Timmermans,et al.  Modelling Complex Activity‐Travel Scheduling Decisions: Procedure for the Simultaneous Estimation of Activity Generation and Duration Functions , 2011 .

[3]  T. Arentze,et al.  The Impact of Simplification in a Sequential Rule-Based Model of Activity-Scheduling Behavior , 2005 .

[4]  K. Axhausen.  Can we ever obtain the data we would like to have , 1998 .

[5]  Sean T. Doherty,et al.  Examining the Nature and Extent of the Activity-Travel Preplanning Decision Process , 2008 .

[6]  David Carpenter,et al.  Use of Internet-Based Household Travel Diary Survey Instrument , 2002 .

[7]  Tommy Gärling,et al.  Computer Simulation of Household Activity Scheduling , 1998 .

[8]  Tommy Gärling,et al.  The role of anticipated time pressure in activity scheduling , 1999 .

[9]  H. Timmermans,et al.  A Utility-Based Analysis of Activity Time Allocation Decisions Underlying Segmented Daily Activity–Travel Patterns , 2005 .

[10]  Ta Theo Arentze,et al.  Analysing space-time behaviour: new approaches to old problems , 2002 .

[11]  Peter Loukopoulos,et al.  Choices of Activity- and Travel-Change Options for Reduced Car Use , 2005 .

[12]  Doina Olaru,et al.  Modelling behavioural rules for daily activity scheduling using fuzzy logic , 2005 .

[13]  Matthew J. Roorda,et al.  Analysis of Planning Decisions during the Activity-Scheduling Process , 2008 .

[14]  Ta Theo Arentze,et al.  Implementation of a model of dynamic activity-travel rescheduling decisions: an agent-based micro-simulation framework , 2005 .

[15]  Geert Wets,et al.  Fitting S-Shaped Activity Utility Functions Based on Stated-Preference Data , 2006 .

[16]  Kay W. Axhausen,et al.  The Development of a Unified Modeling Framework for the Household Activity-Travel Scheduling Process , 1999 .

[17]  D. Ettema,et al.  If You Pick up the Children, I'll Do the Groceries: Spatial Differences in between-Partner Interactions in out-of-Home Household Activities , 2007 .

[18]  Johanna Zmud,et al.  Impact of Internet Retrieval for Reducing Nonresponse in a Household Travel Survey , 2003 .

[19]  T. Arentze,et al.  A need-based model of multi-day, multi-person activity generation , 2009 .

[20]  Michael G. McNally,et al.  On the Structure of Weekly Activity/Travel Patterns , 2003 .