Estimating the influence of common disruptions on urban rail transit networks |
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| Affiliation: | 1. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China;2. School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China;1. Department of Civil Engineering, The Catholic University of America, 620 Michigan Ave., N.E., Washington, DC 20064, United States;2. Langan Engineering, 2700 Kelly Rd, Warrington, PA 18976, United States;1. State key lab of rail traffic control & safety, Beijing Jiaotong University, Beijing, 100044, PR China;2. School of Traffic and Transportation, Beijing Jiaotong University, Beijing, 100044, PR China;1. MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China;2. Department of Civil and Environmental Engineering, National University of Singapore, 117576, Singapore;1. College of Transport and Communications, Shanghai Maritime University, Shanghai 201306, China;2. MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China;3. Griffith School of Engineering, Griffith University, Gold Coast, 4222 QLD, Australia;1. State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;2. Transportation Research Center, Shanghai Jiao Tong University, Shanghai 200240, China;1. Department of Transport and Planning, Delft University of Technology, The Netherlands;2. Delft Institute of Applied Mathematics, Delft University of Technology, The Netherlands |
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| Abstract: | With the continuous expansion of urban rapid transit networks, disruptive incidents—such as station closures, train delays, and mechanical problems—have become more common, causing such problems as threats to passenger safety, delays in service, and so on. More importantly, these disruptions often have ripple effects that spread to other stations and lines. In order to provide better management and plan for emergencies, it has become important to identify such disruptions and evaluate their influence on travel times and delays. This paper proposes a novel approach to achieve these goals. It employs the tap-in and tap-out data on the distribution of passengers from smart cards collected by automated fare collection (AFC) facilities as well as past disruptions within networks. Three characteristic types of abnormal passenger flow are divided and analyzed, comprising (1) “missed” passengers who have left the system, (2) passengers who took detours, and (3) passengers who were delayed but continued their journeys. In addition, the suggested computing method, serving to estimate total delay times, was used to manage these disruptions. Finally, a real-world case study based on the Beijing metro network with the real tap-in and tap-out passenger data is presented. |
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| Keywords: | Urban rail transit network Disruptions AFC data |
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