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A two-stage resilience promotion approach for urban rail transit networks based on topology enhancement and recovery optimization

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  • Xu, Chen
  • Xu, Xueguo

Abstract

Uncertain disruptions in urban rail transit (URT) networks can inflict significant damage to their structure and functionality. Enhancing network resilience has emerged as a critical solution to mitigate these challenges. This paper introduces a two-stage approach to bolster the resilience of large and intricate URT networks by focusing on pre-failure network topology enhancement and post-failure recovery sequence optimization. The assessment of network resilience encompasses the entire resilience process from the onset of a failure to the end of the failure to the completion of the recovery, and the resilience loss triangle model was employed as a valuable tool for assessing network resilience. Meanwhile, an improved resilience evaluation metric was proposed, which uses the global efficiency ratio as the network performance indicator. Taking the Shanghai metro (SHM) system as a case study, a distance-weighted network model was constructed to validate the effectiveness of the proposed resilience enhancement method. Based on the network failure simulation, vulnerable stations in the SHM network were identified and the influence of different metro lines was evaluated. Moreover, considering the existing network topology and practical planning, the topology optimization scheme for adding an outer loop line in the SHM network was developed. The results demonstrate that the topology-optimized network can effectively enhance the network resilience and protect the vulnerable nodes. Additionally, corresponding optimal recovery sequences were proposed for the failure of a single node, multiple nodes, and multiple edges. An interesting finding is that the optimal recovery order is not dependent on the node degree but rather associated with the node vulnerability. Furthermore, it is demonstrated that optimizing the post-failure recovery sequence helps reduce network resilience loss, whereas improving the network topology prior to failure plays a more significant role in promoting resilience. Hence, the proposed methodology and research findings can serve as a valuable reference for the development planning and emergency management of public transportation systems.

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  • Xu, Chen & Xu, Xueguo, 2024. "A two-stage resilience promotion approach for urban rail transit networks based on topology enhancement and recovery optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 635(C).
    • Handle: RePEc:eee:phsmap:v:635:y:2024:i:c:s0378437124000049
    DOI: 10.1016/j.physa.2024.129496
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    as
    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. C. von Ferber & T. Holovatch & Yu. Holovatch & V. Palchykov, 2009. "Public transport networks: empirical analysis and modeling," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 68(2), pages 261-275, March.
    3. 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.
    4. 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).
    5. Du, Zhouyang & Tang, Jinjun & Qi, Yong & Wang, Yiwei & Han, Chunyang & Yang, Yifan, 2020. "Identifying critical nodes in metro network considering topological potential: A case study in Shenzhen city—China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    6. Wang, Shuliang & Gu, Xifeng & Luan, Shengyang & Zhao, Mingwei, 2021. "Resilience analysis of interdependent critical infrastructure systems considering deep learning and network theory," International Journal of Critical Infrastructure Protection, Elsevier, vol. 35(C).
    7. Yingying Xing & Jian Lu & Shendi Chen, 2016. "Weighted Complex Network Analysis of Shanghai Rail Transit System," Discrete Dynamics in Nature and Society, Hindawi, vol. 2016, pages 1-8, August.
    8. Zhang, Jianhua & Xu, Xiaoming & Hong, Liu & Wang, Shuliang & Fei, Qi, 2011. "Networked analysis of the Shanghai subway network, in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4562-4570.
    9. Henry, Devanandham & Emmanuel Ramirez-Marquez, Jose, 2012. "Generic metrics and quantitative approaches for system resilience as a function of time," Reliability Engineering and System Safety, Elsevier, vol. 99(C), pages 114-122.
    10. 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).
    11. Fang, Yi-Ping & Sansavini, Giovanni, 2019. "Optimum post-disruption restoration under uncertainty for enhancing critical infrastructure resilience," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 1-11.
    12. 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.
    13. 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.
    14. Jiangang Shi & Shiping Wen & Xianbo Zhao & Guangdong Wu, 2019. "Sustainable Development of Urban Rail Transit Networks: A Vulnerability Perspective," Sustainability, MDPI, vol. 11(5), pages 1-24, March.
    15. Duncan J. Watts & Steven H. Strogatz, 1998. "Collective dynamics of ‘small-world’ networks," Nature, Nature, vol. 393(6684), pages 440-442, June.
    16. Cao, Xian-Bin & Hong, Chen & Du, Wen-Bo & Zhang, Jun, 2013. "Improving the network robustness against cascading failures by adding links," Chaos, Solitons & Fractals, Elsevier, vol. 57(C), pages 35-40.
    17. Angeloudis, Panagiotis & Fisk, David, 2006. "Large subway systems as complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 367(C), pages 553-558.
    18. Chen, Cynthia & Chen, Jason & Barry, James, 2009. "Diurnal pattern of transit ridership: a case study of the New York City subway system," Journal of Transport Geography, Elsevier, vol. 17(3), pages 176-186.
    19. Cats, Oded, 2017. "Topological evolution of a metropolitan rail transport network: The case of Stockholm," Journal of Transport Geography, Elsevier, vol. 62(C), pages 172-183.
    20. Hong, Liu & Zhong, Xin & Ouyang, Min & Tian, Hui & He, Xiaozheng, 2019. "Vulnerability analysis of public transit systems from the perspective of urban residential communities," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 143-156.
    21. Xueguo Xu & Chen Xu & Wenxin Zhang, 2022. "Research on the Destruction Resistance of Giant Urban Rail Transit Network from the Perspective of Vulnerability," Sustainability, MDPI, vol. 14(12), pages 1-26, June.
    22. Cats, Oded & Krishnakumari, Panchamy, 2020. "Metropolitan rail network robustness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    23. Canbilen Sütiçen, Tuğçe & Batun, Sakine & Çelik, Melih, 2023. "Integrated reinforcement and repair of interdependent infrastructure networks under disaster-related uncertainties," European Journal of Operational Research, Elsevier, vol. 308(1), pages 369-384.
    24. Ouyang, Min, 2017. "A mathematical framework to optimize resilience of interdependent critical infrastructure systems under spatially localized attacks," European Journal of Operational Research, Elsevier, vol. 262(3), pages 1072-1084.
    25. Chen, Chao-Yang & Zhao, Yang & Qin, Huanmei & Meng, Xiangyi & Gao, Jianxi, 2022. "Robustness of interdependent scale-free networks based on link addition strategies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    26. 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).
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