Dynamic flow analysis and crowd management for transfer stations: a case study of Suzhou Metro

Jun Zhang (), Qiannan Ai (), Yuling Ye () and Shejun Deng ()
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Jun Zhang: Yangzhou University
Qiannan Ai: Jiangsu Urban and Rural Construction College
Yuling Ye: College of Transportation Engineering, Tongji University, Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety
Shejun Deng: Yangzhou University

Public Transport, 2024, vol. 16, issue 2, No 10, 619-653

Abstract: Abstract Transfer stations are important nodes in the metro network, and it is of great significance to study the coordinated organization scheme between passenger demand and facility configuration, in order to improve the transportation efficiency. Taking the Dongfangzhimen station of the Suzhou Metro as the research object, this paper starts by analyzing the configuration of service-oriented facilities, and then dissects the spatiotemporal distribution characteristics and individual behavior characteristics of passengers via data processing, where a meticulous analysis has been carried out based on the automatic fare collection data and the field investigation data. On this basis, we have discussed the performance and bottlenecks under the current facility configuration and flow organization scheme, constructed the simulation scene of a representative peak hour in holidays using AnyLogic, and put forward a volume-based path organization scheme considering the coordination with flow demand, as well as a plan of multi-stage crowd management. As evidenced by the results, the improved scheme exhibits a higher adaptability to passenger flow and a greater balance among facility utilization, where the maximum queue length, the average time consumption and the average standing density have been reduced by 31%, 14% and 6%, respectively. The proposed methods of data analysis, flow organization and strategy decision are reliable and applicable to the management of metro transfer stations.

Keywords: Urban rail transit; Passenger flow organization; Demand analysis; Scenario simulation; Crowd management (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s12469-024-00357-8

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