A Key Station Identification Method for Urban Rail Transit: A Case Study of Beijing Subway
Abstract
Congestion occurs and propagates in the stations of urban rail transit, which results in the impendent need to comprehensively evaluate the station performance. Based on complex network theory, a key station identification method is considered. This approach considers both the topology and dynamic operation states of urban rail transit network, such as degree, passenger demand, system capacity and capacity utilization. A case of Beijing urban rail transit is applied to verify the validation of the proposed method. It shows that the method can be helpful to daily passenger flow control and capacity enhancement during peak hours.
References
Zhang M, Du S. Transfer coordination optimization for network operation of urban rail transit based on hierarchical preference. Journal of the China Railway Society. 2009;31(6):9-14.
Lam WHK, Cheung C-Y, Lam CF. A study of crowding effects at the Hong Kong light rail transit stations. Transportation Research Part A: Policy and Practice. 1999;33(5):401-15.
Xu X, Liu J, Li H, Hu J-Q. Analysis of subway station capacity with the use of queueing theory. Transportation Research Part C: Emerging Technologies. 2014;38:28-43.
Kepaptsoglou K, Karlaftis MG. A model for analyzing metro station platform conditions following a service disruption. In: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC; 2010. p. 1789-94.
Wang Z, Chen F, Li X. Comparative analysis and pedestrian simulation evaluation on emergency evacuation test methods for urban rail transit stations. Promet-Traffic & Transportation. 2012;24(6):535-42.
Matulin M, Mrvelj Š, Jelušić N. Two-level Evaluation of Public Transport Performances. Promet – Traffic & Transportation. 2011;23(5):329-39.
Kesten AS, Öğüt KS. A New Passenger-Oriented Performance Measurement Framework for Public Rail Transportation Systems. Promet – Traffic & Transportation. 2014;26(4):299-311.
Román-De la Sancha A, Mayoral JM, Román LI. Modeling Urban Transfer Stations Efficiency. Procedia Computer Science. 2016;83:18-25.
Pjevčević D, Radonjić A, Hrle Z, Čolić V. DEA Window Analysis for Measuring Port Efficiencies in Serbia. Promet – Traffic & Transportation. 2012;24(1):63-72.
Zhang Q, Han B, Li D. Modeling and simulation of passenger alighting and boarding movement in Beijing metro stations. Transportation Research Part C: Emerging Technologies. 2008;16(5):635-49.
Chen D, Lu L, Shang M-S, Zhang Y-C, Zhou T. Identifying influential nodes in complex networks. Physica A: Statistical Mechanics and its Applications. 2012;391(4):1777-87.
Sheikhahmadi A, Nematbakhsh MA, Shokrollahi A. Improving detection of influential nodes in complex networks. Physica A: Statistical Mechanics and its Applications. 2015;436:833-45.
Yang Y, Liu Y, Zhou M, Li F, Sun C. Robustness assessment of urban rail transit based on complex network theory: A case study of the Beijing Subway. Safety Science. 2015;79:149-62.
Murali P, Ordóñez F, Dessouky MM. Modeling strategies for effectively routing freight trains through complex networks. Transportation Research Part C: Emerging Technologies. 2016;70:197-213.
Miler M, Medak D, Odobašić D. The shortest path algorithm performance comparison in graph and relational database on a transportation network. Promet – Traffic & Transportation. 2014;26(1):75-82.
Zhang H, Gu C, Gu L, Zhang Y. The evaluation of tourism destination competitiveness by TOPSIS & information entropy - A case in the Yangtze River Delta of China. Tourism Management. 2011;32(2):443-51.
Jacura M, Týfa L. Utilisation of Decision Tables for Proposal of Transfer Node Conception. Promet – Traffic & Transportation. 2012,24(5):425–31.
Xiao X. Risk and safety assessment of urban rail transit networks operation – a topology based approach. Beijing Jiaotong University; 2014.
Xu X, Liu J, Li H, Jiang M. Capacity-oriented passenger flow control under uncertain demand: Algorithm development and real-world case study. Transportation Research Part E: Logistics and Transportation Review. 2016;87:130-48.
Liu J, Zhou X. Capacitated transit service network design with boundedly rational agents. Transportation Research Part B: Methodological. 2016;93:225-50.
Copyright (c) 2017 Shiwei Sun, Haiying Li, Xinyue Xu
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