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Enhancing robustness of metro networks using strategic defense

Author

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  • Zhu, Weihua
  • Liu, Kai
  • Wang, Ming
  • Yan, Xiaoyong

Abstract

The safety of metro networks has recently attracted great attention. In this study, we investigated how to allocate security resources in a metro network to enhance its robustness through a defense-attack approach. For each defense-attack model, the defense strategy was first applied to protect a certain proportion of influential nodes in the network and then an attack was executed on the protected network. The influential nodes were identified by eight strategies, one of which, the breadth-tree coefficient strategy, is proposed as a new approach. Simulations of five metro networks showed that attacks on nodes with the highest betweenness centrality value are the most effective for disrupting a metro network, while the robustness of the metro network can be enhanced more efficiently with the proposed breadth-tree coefficient strategy. We also found that the robustness of metro networks benefits less from scattered protected metro stations, since some neglected weakly connected nodes emerge among the optimal influencers. The study also provided insights into how topological features influence the robustness of metro networks. It demonstrated that loop lines and smaller transfer stations can provide more options for alternative routes and be less vulnerable to targeted attacks, which makes the metro network more robust in the face of accidents and terrorist attacks. Our study provides useful information for metro network managers to take decisive actions on how to allocate security resources to increase the robustness of their metro network and design a more robust metro system.

Suggested Citation

  • Zhu, Weihua & Liu, Kai & Wang, Ming & Yan, Xiaoyong, 2018. "Enhancing robustness of metro networks using strategic defense," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 1081-1091.
  • Handle: RePEc:eee:phsmap:v:503:y:2018:i:c:p:1081-1091
    DOI: 10.1016/j.physa.2018.08.109
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    1. Gert Sabidussi, 1966. "The centrality index of a graph," Psychometrika, Springer;The Psychometric Society, vol. 31(4), pages 581-603, December.
    2. 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.
    3. Lee, Keumsook & Jung, Woo-Sung & Park, Jong Soo & Choi, M.Y., 2008. "Statistical analysis of the Metropolitan Seoul Subway System: Network structure and passenger flows," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(24), pages 6231-6234.
    4. Hao, Yao-hui & Han, Ji-hong & Lin, Yi & Liu, Lin, 2016. "Vulnerability of complex networks under three-level-tree attacks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 674-683.
    5. Latora, Vito & Marchiori, Massimo, 2002. "Is the Boston subway a small-world network?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 314(1), pages 109-113.
    6. Liu, Kai & Yan, Xiaoyong, 2018. "Current-flow efficiency of networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 463-471.
    7. Réka Albert & Hawoong Jeong & Albert-László Barabási, 2000. "Error and attack tolerance of complex networks," Nature, Nature, vol. 406(6794), pages 378-382, July.
    8. Liu, Ying & Tang, Ming & Zhou, Tao & Do, Younghae, 2016. "Identify influential spreaders in complex networks, the role of neighborhood," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 289-298.
    9. Linyuan Lü & Tao Zhou & Qian-Ming Zhang & H. Eugene Stanley, 2016. "The H-index of a network node and its relation to degree and coreness," Nature Communications, Nature, vol. 7(1), pages 1-7, April.
    10. Yingying Xing & Jian Lu & Shengdi Chen & Sunanda Dissanayake, 2017. "Vulnerability analysis of urban rail transit based on complex network theory: a case study of Shanghai Metro," Public Transport, Springer, vol. 9(3), pages 501-525, October.
    11. Sybil Derrible & Christopher Kennedy, 2010. "Characterizing metro networks: state, form, and structure," Transportation, Springer, vol. 37(2), pages 275-297, March.
    12. 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.
    13. Jin, Jian Gang & Tang, Loon Ching & Sun, Lijun & Lee, Der-Horng, 2014. "Enhancing metro network resilience via localized integration with bus services," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 63(C), pages 17-30.
    14. Flaviano Morone & Hernán A. Makse, 2015. "Influence maximization in complex networks through optimal percolation," Nature, Nature, vol. 524(7563), pages 65-68, August.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

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    2. Weihua Zhu & Shoudong Wang & Shengli Liu & Xueying Gao & Pengchong Zhang & Lixiao Zhang, 2023. "Reliability and Robustness Assessment of Highway Networks under Multi-Hazard Scenarios: A Case Study in Xinjiang, China," Sustainability, MDPI, vol. 15(6), pages 1-15, March.
    3. B. G. Tóth, 2021. "The effect of attacks on the railway network of Hungary," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(2), pages 567-587, June.
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    5. Zhang, Mengyao & Huang, Tao & Guo, Zhaoxia & He, Zhenggang, 2022. "Complex-network-based traffic network analysis and dynamics: A comprehensive review," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).

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