Measuring and Estimating Suppressed Travel with Enhanced Activity–Travel Diaries

Suppressed travel and the related phenomenon of latent transportation demand are important from the point of view of the implementation of traffic–demand management strategies. However, the measurement and estimation of suppressed travel is a nontrivial task that cannot be achieved with the commonly used activity–travel diaries of executed activity and travel episodes. This paper develops an empirical approach for investigating suppressed travel that uses enhanced activity–travel diaries consisting of both a planning and an execution phase. This methodology is applied to data from a 7-day survey in Flanders, Belgium. First, suppressed travel is investigated by observing trip episodes that were planned but not executed. Next, a mixed logit model is built to estimate the probability that a previously planned trip is discarded (i.e., not executed). By means of this model, the household, individual, schedule, activity, and trip attributes that significantly contribute to travel suppression (i.e., the nonexecution of previously planned travel episodes) are identified. The presence of suppressed trips is an indication of the presence of latent transportation demand. Hence, the previously identified attributes can also be interpreted as contributing to latent transportation demand. In addition, the study illustrates that, by considering the special case of suppressed travel corresponding to nonsuppressed activities, the clustering of activities at geographic locations can be investigated. This phenomenon is identified as a special case of trip chaining and, as such, the attributes that significantly influence this form of trip chaining can also be identified by means of the proposed approach.

[1]  T. Loughin.  SAS® for Mixed Models, 2nd edition Edited by Littell, R. C., Milliken, G. A., Stroup, W. W., Wolfinger, R. D., and Schabenberger, O. , 2006 .

[2]  Hjp Harry Timmermans,et al.  A learning-based transportation oriented simulation system , 2004 .

[3]  Matthew J. Roorda,et al.  Strategies for Resolving Activity Scheduling Conflicts: An Empirical Analysis , 2005 .

[4]  Sean T. Doherty,et al.  Mixed Logit Model of Activity-Scheduling Time Horizon Incorporating Spatial–Temporal Flexibility Variables , 2005 .

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

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

[7]  M. Fishbein,et al.  Factors influencing behavior and behavior change. , 2000 .

[8]  Ta Theo Arentze,et al.  Experiences with developing ALBATROSS: a learning-based transportation oriented simulation system , 1998 .

[9]  Sean T. Doherty,et al.  Analysis of Factors Affecting the Frequency and Type of Activity Schedule Modification , 2005 .

[10]  Davy Janssens,et al.  Dynamic Activity-Travel Diary Data Collection Using a GPS-Enabled Personal Digital Assistant , 2006 .

[11]  R. Kitamura.  An evaluation of activity-based travel analysis , 1988 .

[12]  Ta Theo Arentze,et al.  Modeling Learning and Evolutionary Adaptation Processes in Activity Settings: Theory and Numerical Simulations , 2000 .

[13]  Eric J. Miller,et al.  Strategies for Resolving Activity Scheduling Conflicts , 2005 .

[14]  Harry Timmermans,et al.  Changing the duration of activities in resolving scheduling conflicts , 2008 .

[15]  Abolfazl Mohammadian,et al.  Modeling activity scheduling time horizon: Duration of time between planning and execution of pre-planned activities , 2006 .

[16]  Harry Timmermans,et al.  Changing the timing of activities in resolving Scheduling Conflicts , 2006 .

[17]  E. Miller,et al.  A computerized household activity scheduling survey , 2000 .

[18]  C H Chang-Hyeon Joh,et al.  Measuring and predicting adaptation in multidimensional activity-travel patterns , 2004 .

[19]  Ta Theo Arentze,et al.  Modeling Effects of Anticipated Time Pressure on Execution of Activity Programs , 2001 .

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

[21]  Davy Janssens,et al.  Field Evaluation of Personal Digital Assistant Enabled by Global Positioning System , 2008 .

[22]  J. Polak,et al.  Empirical Analysis of Factors Affecting the Resolution of Activity-Scheduling Conflicts , 2005 .

[23]  Toshiyuki Yamamoto,et al.  Florida activity mobility simulator - Overview and preliminary validation results , 2005 .

[24]  Sean T. Doherty,et al.  2005 Fred Burggraf Award, Planning and Environment: How Far in Advance Are Activities Planned?: Measurement Challenges and Analysis , 2005 .

[25]  Michael G. McNally,et al.  Experiments with Computerized Self-Administrative Activity Survey , 2001 .

[26]  Matthew J. Roorda,et al.  Prototype Model of Household Activity-Travel Scheduling , 2003 .

[27]  Davy Janssens,et al.  Activity Travel Planning and Rescheduling Behavior , 2009 .

[28]  Matthew J. Roorda,et al.  Stated Adaptation Survey of Activity Rescheduling , 2007 .

[29]  Dick Ettema,et al.  Theories and models of activity patterns , 1997 .