Within day rescheduling microsimulation combined with macrosimulated traffic |
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| Institution: | 1. Université Lille Nord de France, IFSTTAR, COSYS, LEOST, rue Élisée Reclus 20, 59666 Villeneuve d’Ascq, Lille, France;2. Roma Tre University, Department of Engineering, Section of Computer Science and Automation, via della Vasca Navale, 79, 00146 Rome, Italy;3. Université Lille Nord de France, IFSTTAR, COSYS, ESTAS, rue Élisée Reclus 20, 59666 Villeneuve d’Ascq, Lille, France;1. Center for Environmental Resource Management, University of Texas at El Paso, 500 W. University Ave., El Paso, TX 79968, USA;2. School of Nursing, University of Texas at El Paso, 500 W. University Ave., EL Paso, TX 79968, USA;3. Hispanic Health Disparities Research Center, University of Texas at El Paso, 500 W. University Ave., EL Paso, TX 79968, USA;4. Department of Civil Engineering, University of Texas at El Paso, 500 W. University Ave., El Paso, TX 79968, USA;1. Department of Civil Engineering, Faculty of Engineering, Srinakharinwirot University, Ongkharak, Nakhon Nayok, Thailand;2. Department of Civil Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand;3. Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China;1. Urban Transport Systems Laboratory, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland;2. School of Engineering, University of Glasgow, Glasgow G12 8LT, United Kingdom |
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| Abstract: | The concept of rescheduling is essential to activity-based modeling in order to calculate effects of both unexpected incidents and adaptation of individuals to traffic demand management measures. When collaboration between individuals is involved or timetable based public transportation modes are chosen, rescheduling becomes complex. This paper describes a new framework to investigate algorithms for rescheduling at a large scale. The framework allows to explicitly model the information flow between traffic information services and travelers. It combines macroscopic traffic assignment with microscopic simulation of agents adapting their schedules. Perception filtering is introduced to allow for traveler specific interpretation of perceived macroscopic data and for information going unnoticed; perception filters feed person specific short term predictions about the environment required for schedule adaptation. Individuals are assumed to maximize schedule utility. Initial agendas are created by the FEATHERS activity-based schedule generator for mutually independent individuals using an undisturbed loaded transportation network. The new framework allows both actor behavior and external phenomena to influence the transportation network state; individuals interpret the state changes via perception filtering and start adapting their schedules, again affecting the network via updated traffic demand. The first rescheduling mechanism that has been investigated uses marginal utility that monotonically decreases with activity duration and a monotonically converging relaxation algorithm to efficiently determine the new activity timing. The current framework implementation is aimed to support re-timing, re-location and activity re-sequencing; re-routing at the level of the individual however, requires microscopic travel simulation. |
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| Keywords: | Activity-based modeling Rescheduling Schedule adaptation Travel behavior |
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