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Analysis of metro network performance from a complex network perspective

Author

Listed:
  • Wu, Xingtang
  • Dong, Hairong
  • Tse, Chi Kong
  • Ho, Ivan W.H.
  • Lau, Francis C.M.

Abstract

In this paper, the performance of metro networks is studied from a network science perspective. We review the structural efficiency of metro networks on the basis of a passenger’s intuitive routing strategy that optimizes the number of transfers and the distance traveled.A new node centrality measure, called node occupying probability, is introduced for evaluating the level of utilization of stations. The robustness of a metro network is analyzed under several attack scenarios. Six metro networks (Beijing, London, Paris, Hong Kong, Tokyo and New York) are compared in terms of the node occupying probability and a few other performance parameters. Simulation results show that the New York metro system has better topological efficiency, the Tokyo and Hong Kong systems are the most robust under random attack and target attack, respectively.

Suggested Citation

  • Wu, Xingtang & Dong, Hairong & Tse, Chi Kong & Ho, Ivan W.H. & Lau, Francis C.M., 2018. "Analysis of metro network performance from a complex network perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 553-563.
    • Handle: RePEc:eee:phsmap:v:492:y:2018:i:c:p:553-563
    DOI: 10.1016/j.physa.2017.08.074
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    Citations

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    Cited by:

    1. Mussone, L. & Viseh, H. & Notari, R., 2022. "Novel centrality measures and applications to underground networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    2. Kopsidas, Athanasios & Kepaptsoglou, Konstantinos, 2022. "Identification of critical stations in a Metro System: A substitute complex network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    3. Meng, Yangyang & Tian, Xiangliang & Li, Zhongwen & Zhou, Wei & Zhou, Zhijie & Zhong, Maohua, 2020. "Comparison analysis on complex topological network models of urban rail transit: A case study of Shenzhen Metro in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    4. Wei Yu & Xiaofei Ye & Jun Chen & Xingchen Yan & Tao Wang, 2020. "Evaluation Indexes and Correlation Analysis of Origination–Destination Travel Time of Nanjing Metro Based on Complex Network Method," Sustainability, MDPI, vol. 12(3), pages 1-21, February.
    5. Jia, Tao & Liu, Wenxuan & Liu, Xintao, 2021. "A cross-city exploratory analysis of the robustness of bus transit networks using open-source data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    6. Noguchi, Hiroki & Fuse, Masaaki, 2020. "Rethinking critical node problem for railway networks from the perspective of turn-back operation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).
    7. Yangyang Meng & Qingjie Qi & Jianzhong Liu & Wei Zhou, 2022. "Dynamic Evolution Analysis of Complex Topology and Node Importance in Shenzhen Metro Network from 2004 to 2021," Sustainability, MDPI, vol. 14(12), pages 1-19, June.
    8. Liu, Xiaolei & Lei, Zengxiang & Duan, Zhengyu, 2024. "Assessing metro network vulnerability with turn-back operations: A Monte Carlo method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 646(C).
    9. Xiao Feng & Shiwei He & Xuchao Chen & Guangye Li, 2021. "Mitigating the vulnerability of an air-high-speed railway transportation network: From the perspective of predisruption response," Journal of Risk and Reliability, , vol. 235(3), pages 474-490, June.
    10. Abdelaty, Hatem & Mohamed, Moataz & Ezzeldin, Mohamed & El-Dakhakhni, Wael, 2022. "Temporal robustness assessment framework for city-scale bus transit networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    11. Hong, Liu & Ye, Bowen & Yan, Han & Zhang, Hui & Ouyang, Min & (Sean) He, Xiaozheng, 2019. "Spatiotemporal vulnerability analysis of railway systems with heterogeneous train flows," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 725-744.
    12. Hui Xu & Liudan Jiao & Shulin Chen & Milan Deng & Ningxin Shen, 2018. "An Innovative Approach to Determining High-Risk Nodes in a Complex Urban Rail Transit Station: A Perspective of Promoting Urban Sustainability," Sustainability, MDPI, vol. 10(7), pages 1-17, July.
    13. Chan, Ho-Yin & Chen, Anthony & Li, Guoyuan & Xu, Xiangdong & Lam, William, 2021. "Evaluating the value of new metro lines using route diversity measures: The case of Hong Kong's Mass Transit Railway system," Journal of Transport Geography, Elsevier, vol. 91(C).

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