An Activity-Based Multimodal Model Structure to assess Transportation Management Strategies for Urban Emergencies

Paper authored by Jeroen P.T. van der Gun, Adam J. Pel and Bart van Arem in 2014.

Conference contribution presented at the Joint Chinese-Dutch Seminar on Transportation Management and Travel Behaviour for Urban Emergencies: Past, Present, and Future Research in Shanghai, China on 25–27 June 2014.

Chapter included in Proceedings of Emergency Transport Management, edited by Huizhao Tu and Adam J. Pel, pages 79–83, published by TRAIL Research School in Delft, The Netherlands, ISBN 978-90-78271-08-6.

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Abstract

There are many kinds of disasters that can severely impact the transportation system of an urbanized region. Transportation authorities therefore need to develop management strategies to adequately deal with such emergencies. In this paper, we discuss the structure of a simulation model that can be used to assess a candidate strategy. We model the choice behavior of the population during the emergency using a microscopic, activity-based approach and simulate the performance of the multimodal transportation network with a macroscopic dynamic network loading module, taking into account what happens on a normal day. The disaster plan under consideration may contain adaptive elements and is assessed in a robust way by testing a range of possible scenarios.

Keywords: urban emergencies, transportation, choice models, activity-based models, multimodal dynamic network loading, robust optimization.

Citation: Van der Gun, J.P.T., Pel, A.J., Van Arem, B. (2014). An Activity-Based Multimodal Model Structure to assess Transportation Management Strategies for Urban Emergencies. Paper in: Tu, H., Pel, A.J. (Eds.). Proceedings of Emergency Transport Management, pp. 79–83. Delft, The Netherlands: TRAIL Research School. ISBN 978-90-78271-08-6. Presented at the Joint Chinese-Dutch Seminar on Transportation Management and Travel Behaviour for Urban Emergencies: Past, Present, and Future Research. Shanghai, China, 25–27 June.