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A double-weighted vulnerability assessment model for metrorail transit networks and its application in Shanghai metro

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  • Zhang, Yanjie
  • Ayyub, Bilal M.
  • Saadat, Yalda
  • Zhang, Dongming
  • Huang, Hongwei

Abstract

The vulnerability assessment of urban metrorail transit (or simply metro) networks is of great significance for a large scaled metro network to ensure the safe and sustainable operation. Comparing to the traditional model, this paper presents a modified double-weighted vulnerability analysis model for metro networks by taking account of the path distance and passenger flow as they are the two key features of a metro network. In this model, a link-weighted adjacency matrix was established based on which a node-weighted network efficiency was formulated considering the distance of path and the ridership between any two metro stations. The model was then applied to Shanghai metro to examine its vulnerability with failure scenarios at different scales from node failure, link failure to line failure. The network vulnerability was examined by the decrease in network efficiency. Results show that metro stations with high node degree and large ridership greatly contribute the influence on global network efficiency. It is identified from the double-weighted network model that Shanghai metro is more vulnerable to the failure of Lines 8 to 10, rather than Line 1, Line 2 and Line 11 obtained from the traditional unweighted network model. In this study, the most vulnerable metro stations, links and lines can be derived with consideration of path distance and passenger flow, which could provide guidance for reasonable resource allocation in system maintenance and aid the decision on recovery strategies for resilience of metrorail system.

Suggested Citation

  • Zhang, Yanjie & Ayyub, Bilal M. & Saadat, Yalda & Zhang, Dongming & Huang, Hongwei, 2020. "A double-weighted vulnerability assessment model for metrorail transit networks and its application in Shanghai metro," International Journal of Critical Infrastructure Protection, Elsevier, vol. 29(C).
  • Handle: RePEc:eee:ijocip:v:29:y:2020:i:c:s1874548220300226
    DOI: 10.1016/j.ijcip.2020.100358
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    2. Meng, Yangyang & Zhao, Xiaofei & Liu, Jianzhong & Qi, Qingjie & Zhou, Wei, 2023. "Data-driven complexity analysis of weighted Shenzhen Metro network based on urban massive mobility in the rush hours," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
    3. Ma, Min & Hu, Dawei & Chien, Steven I-Jy & Liu, Jie & Yang, Xing & Ma, Zhuanglin, 2022. "Evolution assessment of urban rail transit networks: A case study of Xi’an, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    4. 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.
    5. Yeh, Cheng-Ta & Lin, Yi-Kuei & Yeng, Louis Cheng-Lu & Huang, Pei-Tzu, 2021. "Reliability evaluation of a multistate railway transportation network from the perspective of a travel agent," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    6. Martinez-Pastor, Beatriz & Nogal, Maria & O’Connor, Alan & Teixeira, Rui, 2022. "Identifying critical and vulnerable links: A new approach using the Fisher information matrix," International Journal of Critical Infrastructure Protection, Elsevier, vol. 39(C).
    7. Liu, Kai & Zhu, Jiatong & Wang, Ming, 2021. "An event-based probabilistic model of disruption risk to urban metro networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 147(C), pages 93-105.

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