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Innovations in Travel Demand Modeling, Volume 2: Papers (2008)

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Suggested Citation:"T57054 txt_076.pdf." National Academies of Sciences, Engineering, and Medicine. 2008. Innovations in Travel Demand Modeling, Volume 2: Papers. Washington, DC: The National Academies Press. doi: 10.17226/13678.
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CONCLUSIONS AND DISCUSSION This paper has reported progress and plans in the devel- opment, testing, and implementation of a multiagent activity-based model of (re)scheduling behavior called Aurora. An operational and extended version of this model will be developed specifically for Flanders, Bel- gium, under the acronym Feathers. Data collection for estimating the various components is on its way. Plans are to report the first empirical results in the near future. Unlike the activity-based models mentioned in the paper’s introduction, this model has the potential value to simulate short-term dynamics. As such, it should be pri- marily relevant to simulate dynamics in day-to-day traffic flows and their environmental impacts. Its development into a model that can be used for longer-term assessment would require additional components. Such projects are on their way as well but not part of Feathers at this stage. The future will then tell whether the greater complexity implied by these and other extensions will be feasible, not only from a modeling and computational standpoint but also in relation to acceptance by practitioners and policy makers. ACKNOWLEDGMENT The research program presented in this paper was sup- ported by the Institute for the Promotion of Innovation by Science and Technology in Flanders. REFERENCES Arentze, T. A., C. Pelizaro, and H. J. P. Timmermans. 2005. Implementation of a Model of Dynamic Activity-Travel Rescheduling Decisions: An Agent-Based Micro-Simula- tion Framework. Proc., Computers in Urban Planning and Urban Management Conference (CD-ROM), London. Arentze, T. A., and H. J. P. Timmermans. 2000. Albatross: A Learning-Based Transportation Oriented Simulation Sys- tem. European Institute of Retailing and Services Studies, Eindhoven, Netherlands. Arentze, T. A., and H. J. P. Timmermans. 2004. A Theoretical Framework for Modeling Activity-Travel Scheduling Deci- sions in Non-Stationary Environments Under Conditions of Uncertainty and Learning. Proc., International Confer- ence on Activity-Based Analysis (CD-ROM), Maastricht, Netherlands. Arentze, T. A., and H. J. P. Timmermans. 2005a. Albatross 2: A Learning-Based Transportation Oriented Simulation Sys- tem. European Institute of Retailing and Services Studies. Eindhoven, Netherlands. Arentze, T. A., and H. J. P. Timmermans. 2005b. Representing Mental Maps and Cognitive Learning in Micro-Simulation Models of Activity-Travel Choice Dynamics. Transporta- tion, Vol. 32, pp. 321–340. Arentze, T. A., and H. J. P. Timmermans. 2006. Multi-Agent Models of Spatial Cognition, Learning, and Complex Choice Behavior in Urban Environments. In Complex Arti- ficial Environments (J. Portugal, ed.), Spring Verlag, Berlin, pp. 181–200. Bellemans, T., B. Kochan, D. Janssens, and G. Wets. 2005. Collecting Activity-Travel Diary Data by Means of a GPS- Enabled Personal Digital Assistant. Colloquium Vervoers- planologisch Speurwerk, Vol. 32, Nov. 24–25, pp. 2111–2131. Bhat, C. R., J. Y. Guo, S. Srinivasan, and A. Sivakumar. 2004. A Comprehensive Micro-Simulator for Daily Activity-Travel Patterns. Proc., Conference on Progress in Activity-Based Models (CD-ROM), Maastricht, Netherlands, May 28–31. Han, Q., and H. J. P. Timmermans. 2006. Interactive Learning in Transportation Networks Under Conditions of with Uncertainty, Bounded Rationality, And Strategic Choice Behavior: Quantal Response Model. In Transportation Research Record: Journal of the Transportation Research Board, No. 1964, Transportation Research Board of the National Academies, Washington, D.C., pp. 27–34. Joh, C.-H., T. A. Arentze, and J. J. P. Timmermans. 2003. Understanding Activity Scheduling and Rescheduling Behaviour: Theory and Numerical Simulation. In Model- ling Geographical Systems (B. N. Boots, A. Okabe, and R. Thomas, eds.), Kluwer Academic Publishers, Dordrecht, Netherlands, pp. 73–95. Joh, C.-H., T. A. Arentze, and H. J. P. Timmermans. 2004. Activity-Travel Rescheduling Decisions: Empirical Estima- tion of the Aurora Model. In Transportation Research Record: Journal of the Transportation Research Board, No. 1898, Transportation Research Board of the National Acad- emies, Washington, D.C., pp. 10–18. Kochan, B., T. Bellemans, D. Janssens, and G. Wets. 2006. Dynamic Activity-Travel Diary Data Collection: Using a GPS-Enabled Personal Digital Assistant. In Conference Proceedings 42: Innovations in Travel Modeling Confer- ence, Transportation Research Board of the National Acad- emies, Washington, D.C., pp. 94–97. Pendyala, R. M., R. Kitamura, A. Kikuchi, T. Yamamoto, and S. Fujii. 2005. Florida Activity Mobility Simulator: Overview and Preliminary Validation Results. In Trans- portation Research Record: Journal of the Transportation Research Board, No. 1921, Transportation Research Board of the National Academies, Washington, D.C., pp. 123–130. Sun, Z., T. A. Arentze, and H. J. P. Timmermans. 2005. Mod- eling the Impact of Travel Information on Activity-Travel Rescheduling Decisions Under Conditions of Travel Time Uncertainty. In Transportation Research Record: Journal of the Transportation Research Board, No. 1926, Trans- portation Research Board of the National Academies, Washington, D.C., pp. 79–87. 76 INNOVATIONS IN TRAVEL DEMAND MODELING, VOLUME 2

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TRB Conference Proceedings 42, Innovations in Travel Demand Modeling, Volume 2: Papers includes the papers that were presented at a May 21-23, 2006, conference that examined advances in travel demand modeling, explored the opportunities and the challenges associated with the implementation of advanced travel models, and reviewed the skills and training necessary to apply new modeling techniques. TRB Conference Proceedings 42, Innovations in Travel Demand Modeling, Volume 1: Session Summaries is available online.

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