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Open AccessArticle

Simulation of Metro Congestion Propagation Based on Route Choice Behaviors Under Emergency-Caused Delays

by Xingchuan Wang 1,2, Enjian Yao 1,2,* and Shasha Liu 3
1
School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
2
Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China
3
Institute of Materials and Systems for Sustainability, Nagoya University, Nagoya 464-8601, Japan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(20), 4210; https://doi.org/10.3390/app9204210
Received: 3 September 2019 / Revised: 1 October 2019 / Accepted: 2 October 2019 / Published: 9 October 2019
(This article belongs to the Special Issue Intelligent Transportation Systems)
Generally, metro emergencies could lead to delays and seriously affect passengers’ trips. The dynamic congestion propagation process under metro emergency-caused delays could be regarded as the aggregation of passengers’ individual travel choices. This paper aims to simulate the congestion propagation process without intervention measures under the metro emergency-caused delays, which is integrated with passengers’ route choice behaviors. First, using a stated preference survey data collected from Guangzhou Metro (GZM) passengers, route choice models are developed based on random regret minimization (RRM) theory under metro emergency conditions. Then, a simulation environment is established using graph cellular automata (graph-CA) with augmented GZM network structure, where an ASEIR (advanced susceptible-exposed-infectious-recovered) model with time delay is proposed as the evolution rule in graph-CA. Furthermore, considering passengers’ routing preferences, a quantified method for the congestion propagation rate is proposed, and the congestion propagation process on a subnetwork of the GZM network is simulated. The simulation results show that metro congestion during peak periods has a secondary increase after the end of the emergency-caused delays, while the congestion during nonpeak hours has a shorter duration and a smaller influence range. The proposed simulation model could clearly reflect the dynamic process of congestion propagation under metro emergencies. View Full-Text
Keywords: metro emergency-caused delays; route choice behavior; congestion propagation simulation; ASEIR model; quantified congestion propagation rate metro emergency-caused delays; route choice behavior; congestion propagation simulation; ASEIR model; quantified congestion propagation rate
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Wang, X.; Yao, E.; Liu, S. Simulation of Metro Congestion Propagation Based on Route Choice Behaviors Under Emergency-Caused Delays. Appl. Sci. 2019, 9, 4210.

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