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Discrete Dynamics in Nature and Society
Volume 2017, Article ID 2890814, 15 pages
Research Article

Simulation-Based Dynamic Passenger Flow Assignment Modelling for a Schedule-Based Transit Network

1School of Traffic and Transportation, Beijing Jiaotong University, No. 3 Shangyuancun, Haidian District, Beijing 100044, China
2College of Engineering and Applied Science, University of Cincinnati, 2600 Clifton Avenue, Cincinnati, OH 45220, USA

Correspondence should be addressed to Xiangming Yao; nc.ude.utjb@mxoay

Received 7 October 2016; Revised 9 January 2017; Accepted 19 January 2017; Published 13 February 2017

Academic Editor: Ricardo López-Ruiz

Copyright © 2017 Xiangming Yao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The online operation management and the offline policy evaluation in complex transit networks require an effective dynamic traffic assignment (DTA) method that can capture the temporal-spatial nature of traffic flows. The objective of this work is to propose a simulation-based dynamic passenger assignment framework and models for such applications in the context of schedule-based rail transit systems. In the simulation framework, travellers are regarded as individual agents who are able to obtain complete information on the current traffic conditions. A combined route selection model integrated with pretrip route selection and entrip route switch is established for achieving the dynamic network flow equilibrium status. The train agent is operated strictly with the timetable and its capacity limitation is considered. A continuous time-driven simulator based on the proposed framework and models is developed, whose performance is illustrated through a large-scale network of Beijing subway. The results indicate that more than 0.8 million individual passengers and thousands of trains can be simulated simultaneously at a speed ten times faster than real time. This study provides an efficient approach to analyze the dynamic demand-supply relationship for large schedule-based transit networks.