Assessing Changes in Travel Behavior Induced by Modified Travel Times: A Stated Adaptation Survey and Modeling Approach

Abstract

The paper reports on research exploring a stated adaptation approach for analyzing changes in travel behavior brought about by changes in travel times. A sample of respondents returned a five-day travel diary, from which one day was selected for further analysis. The conditions of the reported day were modified using predefined heuristics to attain significant changes in the travel time of the reported trips. The respondents were then presented with these hypothetical scenarios in faceto-face interviews and asked to state how the implied changes would have affected their activity scheduling on the specified day and then to adapt their reported schedule.

The data was used to estimate models of scheduling adaptations. A series of models was used to assess the adaptations stated by the respondents. At the core is a Multiple Discrete-Continuous Extreme Value (MDCEV) model, which is used to predict how the respondents compensate for the gained or lost time.

The adaptations to the respondents' schedule were surprisingly sparse despite the substantial changes in generalized costs imposed by the scenarios. The most frequent adaptations were changes in departure time from the home location, and mode or destination changes to recover the lost time. The expected changes, concerning the number of conducted trips and activities and their combination into home-tohome tours, were extremely rare. Respondents were very selective when making adaptations to their routine, even under extreme circumstances.

The paper describes the survey approach, which is to the best of our knowledge novel in its application, and reports the model results of the respondents' reactions to the changes implied in the household interviews and provides an example of the potential application of the results.

Acknowledgements

The authors gratefully acknowledge the financial support of the SBT-Fonds administered by the Swiss Association of Transport Engineers (SVI 2004/012) and the advice of the steering committee, chaired by Michel Simon and including René Zbinden, Samuel Waldvogel, Stefan Dasen and Helmut Honermann. Furthermore, we would like to thank Prof. Michel Bierlaire and Dr. Chandra Bhat for making their software programs available for the estimation of discrete and discrete-continuous choice models, respectively.

Christoph Dobler invested a great amount of time and programming skill into the implementation of the survey software, for which he deserves our gratitude.

The student assistants who recruited the survey respondents and carried out the interviews deserve a grateful mention, namely Sarah Brack, Manuela Hess, Rolf Hug, Tina Lohfing, Ana Pajovic, Alessandra Pellegrini and Janira Perrotta.

Finally, our thanks go the three anonymous reviewers who provided helpful feedback that helped improve the paper.

Additional information

Notes on contributors

Claude Weis

Dr. Claude Weis recently finished his PhD thesis on induced travel demand at the Institute for Transport Planning and Systems (IVT) of ETH Zurich. In his current position as a senior researcher, he is working on various research projects involving stated preference techniques and discrete choice models.

Kay W. Axhausen

Prof. Dr. Kay W. Axhausen is Full Professor for Transport Planning at the Institute for Transport Planning and Systems (IVT) of ETH Zurich. One of his core research topics is the measurement and modeling of travel behavior. His group has built up a set of methods and data sets to explore everyday behavior.