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27 BREAKOUT SESSION Activity- Based Models Theo Arentz, TU Eindhoven, the Netherlands Harry Timmermans, TU Eindhoven, the Netherlands Davy Janssens, Hasselt University, Transportation Research Institute, Belgium Geert Wets, Hasselt University, Transportation Research Institute, Belgium Chandra Bhat, University of Texas at Austin Abdul Pinjari, University of Texas at Austin Naveen Eluru, University of Texas at Austin Ipek Sener, University of Texas at Austin Rachel Copperman, University of Texas at Austin Jessica Guo, University of Wisconsin, Madison Sivaramakrishnan Srinivasan, University of Florida Kay Axhausen, Swiss Federal Institute of Technology Ram Pendyala, University of South Florida Ryuichi Kitamura, Kyoto University Kaira Kikuchi, Kyoto University MODELING SHORT- TERM DYNAMICS IN ACTIVITY- TRAVEL PATTERNS: FROM AURORA TO FEATHERS Theo Arentz, Harry Timmermans, Davy Janssens, and Geert Wets Davy Janssens described an ongoing research program in the Netherlands and Belgium on activity- based travel models. He discussed the Aurora model and the Fore- casting Evolutionary Activity- Travel of Households and Their Environmental Repercussions (Feathers) process. Volume 2 provides a paper on the topic.1 The following points were covered in his presentation. ⢠Several activity- travel demand models, including nested logit models, are in operation. These models tend to focus on activity- travel patterns. Also emerging are more robust, fully operational, activity- based models. Even with these advancements, there is still more to be accomplished to enhance activity- based models and pro- mote their use. Areas of possible improvement include short- term dynamics or rescheduling of travelersâ behav- ior, and incorporating uncertainty, learning, and nonsta- tionary environments. Modeling route choice behavior and the aggregate impact of individual route choice on activity generation and rescheduling represents another area for enhancements. ⢠The Aurora model incorporates some of these ele- ments. Aurora develops an agent- based microsimulation system of dynamic activity- travel choice where agents represent individuals. These individuals have limited knowledge of their environment. An activity schedule is generated for each agent for each day and implements the schedule in space and time. In making trips, individuals may experience congestion and adapt their schedules. 1 See Arentze, T., H. Timmermans, D. Janssens, and G. Wets. Modeling Short- Term Dynamics in Activity- Travel Patterns: From Aurora to Feathers. In Conference Proceedings 42: Innovations in Travel Demand Modeling, Volume 2: Papers, Transportation Research Board of the National Academies, Washington, D.C., 2008, pp. 71â77.
