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Identifying critical metro stations in multiplex network based on D–S evidence theory

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  • Tang, Jinjun
  • Li, Zhitao
  • Gao, Fan
  • Zong, Fang

Abstract

Public transport networks (PTNs) undertake large amount of passenger demand in the urban transport system. Particularly, stations in metro networks with high significance in structure and function are more likely to contribute to passenger transport. Once these stations are under functional failure, it is easy to lead to the collapse of network connectivity. Thus, identifying critical nodes in PTNs is of practical significance for the public transport planning and operation. This study proposes an identification method for the critical nodes in multiplex network by considering the interaction between metro and bus networks. First, metro and bus networks are constructed by L-space and P-space methods, respectively. Then, ridership is extracted to describe the connection between nodes as the weights of links, and the metro-bus multiplex network is then constructed. Furthermore, the identification method in Multiplex Network based on Dempster–Shafer evidence theory (MNDS) is proposed to fuse the significance of nodes in sub-networks, and the critical nodes in the multiplex network are identified. Finally, the PTNs in Shenzhen City, China, is used as a case to demonstrate the feasibility of MNDS. By attacking critical nodes, a comparison is conducted and compared with two traditional identification methods (Weighted Closeness Centrality and Technique for Order Preference by Similarity to Ideal Solution) using two indicators, global efficiency (GE) and the size of the largest connected component (LCC). The results indicate the critical nodes identified by MNDS are of greater significance than those identified by the other two methods. This study provides a feasible method for critical nodes identification in urban public transport system, which can be applied to public transport operation and planning.

Suggested Citation

  • Tang, Jinjun & Li, Zhitao & Gao, Fan & Zong, Fang, 2021. "Identifying critical metro stations in multiplex network based on D–S evidence theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    • Handle: RePEc:eee:phsmap:v:574:y:2021:i:c:s0378437121002909
    DOI: 10.1016/j.physa.2021.126018
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    3. Li, Zhitao & Tang, Jinjun & Zhao, Chuyun & Gao, Fan, 2023. "Improved centrality measure based on the adapted PageRank algorithm for urban transportation multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    4. Wang, Wenhao & Wang, Yanhui & Wang, Guangxing & Li, Man & Jia, Limin, 2023. "Identification of the critical accident causative factors in the urban rail transit system by complex network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
    5. Anupriya, & Graham, Daniel J. & Bansal, Prateek & Hörcher, Daniel & Anderson, Richard, 2023. "Optimal congestion control strategies for near-capacity urban metros: Informing intervention via fundamental diagrams," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    6. Ma, Zhiao & Yang, Xin & Wu, Jianjun & Chen, Anthony & Wei, Yun & Gao, Ziyou, 2022. "Measuring the resilience of an urban rail transit network: A multi-dimensional evaluation model," Transport Policy, Elsevier, vol. 129(C), pages 38-50.
    7. Chen, Junlan & Pu, Ziyuan & Guo, Xiucheng & Cao, Jieyu & Zhang, Fang, 2023. "Multiperiod metro timetable optimization based on the complex network and dynamic travel demand," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).

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