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Vulnerability Assessments of Urban Rail Transit Networks Based on Redundant Recovery

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  • Jianhua Zhang

    (School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou 221116, China)

  • Ziqi Wang

    (School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou 221116, China)

  • Shuliang Wang

    (School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou 221116, China)

  • Shengyang Luan

    (School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou 221116, China)

  • Wenchao Shao

    (School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou 221116, China)

Abstract

Urban rail transit has received much attention in the last two decades, and a significant number of cities have established urban rail transit networks (URTNs). Although URTNs have brought enormous convenience to the daily life of citizens, system failures still frequently occur, therefore the vulnerability of URTNs must be a concern. In this paper, we propose a novel measurement called the node strength parameter to assess the importance of nodes and present a redundant recovery scheme to imitate the system recovery of URTNs subjected to failures. Employing three malicious attacks and taking the Nanjing subway network as the case study, we investigated the network vulnerability under scenarios of different simulated attacks. The results illustrate that passenger in-flow shows the negligible impact on the vulnerability of the node, while out-flow plays a considerable role in the largest strength node-based attack. Further, we find that vulnerability will decrease as passenger out-flow increases, and the vulnerability characteristics are the same with the increase in the construction cost of URTNs. Considering different attack scenarios, the results indicate that the highest betweenness node-based attack will cause the most damage to the system, and increasing the construction cost can improve the robustness of URTNs.

Suggested Citation

  • Jianhua Zhang & Ziqi Wang & Shuliang Wang & Shengyang Luan & Wenchao Shao, 2020. "Vulnerability Assessments of Urban Rail Transit Networks Based on Redundant Recovery," Sustainability, MDPI, vol. 12(14), pages 1-14, July.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:14:p:5756-:d:385824
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    References listed on IDEAS

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    1. Derrible, Sybil & Kennedy, Christopher, 2010. "The complexity and robustness of metro networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(17), pages 3678-3691.
    2. Sun, Daniel (Jian) & Guan, Shituo, 2016. "Measuring vulnerability of urban metro network from line operation perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 348-359.
    3. Abigail Luxton & Marin Marinov, 2020. "Terrorist Threat Mitigation Strategies for the Railways," Sustainability, MDPI, vol. 12(8), pages 1-21, April.
    4. Marinov, Marin & Şahin, İsmail & Ricci, Stefano & Vasic-Franklin, Gordana, 2013. "Railway operations, time-tabling and control," Research in Transportation Economics, Elsevier, vol. 41(1), pages 59-75.
    5. Sun, Huijun & Wu, Jianjun & Wu, Lijuan & Yan, Xiaoyong & Gao, Ziyou, 2016. "Estimating the influence of common disruptions on urban rail transit networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 62-75.
    6. Lu, Qing-Chang, 2018. "Modeling network resilience of rail transit under operational incidents," Transportation Research Part A: Policy and Practice, Elsevier, vol. 117(C), pages 227-237.
    7. Sun, Lishan & Huang, Yuchen & Chen, Yanyan & Yao, Liya, 2018. "Vulnerability assessment of urban rail transit based on multi-static weighted method in Beijing, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 108(C), pages 12-24.
    8. Zhang, Jianhua & Wang, Shuliang & Wang, Xiaoyuan, 2018. "Comparison analysis on vulnerability of metro networks based on complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 72-78.
    9. Daniel (Jian) Sun & Yuhan Zhao & Qing-Chang Lu, 2015. "Vulnerability Analysis of Urban Rail Transit Networks: A Case Study of Shanghai, China," Sustainability, MDPI, vol. 7(6), pages 1-18, May.
    10. Shang, Pan & Li, Ruimin & Guo, Jifu & Xian, Kai & Zhou, Xuesong, 2019. "Integrating Lagrangian and Eulerian observations for passenger flow state estimation in an urban rail transit network: A space-time-state hyper network-based assignment approach," Transportation Research Part B: Methodological, Elsevier, vol. 121(C), pages 135-167.
    11. Praful Potti & Marin Marinov & Edward Sweeney, 2019. "A Simulation Study on the Potential of Moving Urban Freight by a Cross-City Railway Line," Sustainability, MDPI, vol. 11(21), pages 1-19, November.
    12. Zhang, Jianhua & Wang, Meng, 2019. "Transportation functionality vulnerability of urban rail transit networks based on movingblock: The case of Nanjing metro," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    13. Zhang, Jianhua & Zhao, Mingwei & Liu, Haikuan & Xu, Xiaoming, 2013. "Networked characteristics of the urban rail transit networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(6), pages 1538-1546.
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    Cited by:

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    2. 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).
    3. Xiaohong Yin & Jiakun Wu, 2022. "Simulation Study on Topology Characteristics and Cascading Failure of Hefei Subway Network," Sustainability, MDPI, vol. 15(1), pages 1-26, December.
    4. Lu, Qing-Chang & Zhang, Lei & Xu, Peng-Cheng & Cui, Xin & Li, Jing, 2022. "Modeling network vulnerability of urban rail transit under cascading failures: A Coupled Map Lattices approach," Reliability Engineering and System Safety, Elsevier, vol. 221(C).

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