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Identifying the critical nodes in multi-modal transportation network with a traffic demand-based computational method

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  • Wang, Longjian
  • Zhang, Shuichao
  • Szűcs, Gábor
  • Wang, Yonggang

Abstract

In the last decade, there has been an abundance of research on identifying critical nodes in single-modal transportation network, but less for multi-modal transportation network (MTN). In addition, none of them consider the effect of interactions between nodes in a transportation corridor on node importance. For this purpose, a modified weighted k-shell (MWKS) model is proposed for identifying the critical nodes of the MTN, which combines the network topology and traffic demand. It is modified by the differential contribution of the multi-order neighbor nodes embodied by the passenger volume and the transportation corridor idea. Then the effectiveness of MWKS is verified with an example of a real transportation system using susceptible-infected (SI) model and cascading failure model. The analysis proves that MWKS is suitable for identifying critical nodes in the MTN. Furthermore, the proposed contribution attenuation coefficient based on the idea of the transportation corridor outperforms the topology-based and constant. It demonstrates the necessity of capturing the difference in the contribution of each neighbor node based on traffic demand when calculating the node importance. The results can provide a research basis for traffic planning to improve network reliability.

Suggested Citation

  • Wang, Longjian & Zhang, Shuichao & Szűcs, Gábor & Wang, Yonggang, 2024. "Identifying the critical nodes in multi-modal transportation network with a traffic demand-based computational method," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    • Handle: RePEc:eee:reensy:v:244:y:2024:i:c:s0951832024000310
    DOI: 10.1016/j.ress.2024.109956
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    References listed on IDEAS

    as
    1. Hong, Liu & Ouyang, Min & Xu, Min & Hu, Peipei, 2020. "Time-varied accessibility and vulnerability analysis of integrated metro and high-speed rail systems," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    2. He, Zhidong & Navneet, Kumar & van Dam, Wirdmer & Van Mieghem, Piet, 2021. "Robustness assessment of multimodal freight transport networks," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    3. Dui, Hongyan & Chen, Shuanshuan & Wang, Jia, 2021. "Failure-oriented maintenance analysis of nodes and edges in network systems," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    4. Liu, Honglu & Tian, Zhihong & Huang, Anqiang & Yang, Zaili, 2018. "Analysis of vulnerabilities in maritime supply chains," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 475-484.
    5. Du, Yuxian & Gao, Cai & Hu, Yong & Mahadevan, Sankaran & Deng, Yong, 2014. "A new method of identifying influential nodes in complex networks based on TOPSIS," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 399(C), pages 57-69.
    6. Zhao, Jie & Wang, Yunchuan & Deng, Yong, 2020. "Identifying influential nodes in complex networks from global perspective," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).
    7. Mishra, Sabyasachee & Welch, Timothy F. & Jha, Manoj K., 2012. "Performance indicators for public transit connectivity in multi-modal transportation networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(7), pages 1066-1085.
    8. Liu, Tao & Bai, Guanghan & Tao, Junyong & Zhang, Yun-An & Fang, Yining & Xu, Bei, 2022. "Modeling and evaluation method for resilience analysis of multi-state networks," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    9. Li, Tao & Rong, Lili, 2021. "Impacts of service feature on vulnerability analysis of high-speed rail network," Transport Policy, Elsevier, vol. 110(C), pages 238-253.
    10. 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).
    11. Huang, Wencheng & Zhou, Bowen & Yu, Yaocheng & Yin, Dezhi, 2021. "Vulnerability analysis of road network for dangerous goods transportation considering intentional attack: Based on Cellular Automata," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    12. Chen, Duanbing & Lü, Linyuan & Shang, Ming-Sheng & Zhang, Yi-Cheng & Zhou, Tao, 2012. "Identifying influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1777-1787.
    13. Psaltoglou, Artemis & Calle, Eusebi, 2018. "Enhanced connectivity index – A new measure for identifying critical points in urban public transportation networks," International Journal of Critical Infrastructure Protection, Elsevier, vol. 21(C), pages 22-32.
    14. Jafino, Bramka Arga, 2021. "An equity-based transport network criticality analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 204-221.
    15. Lechen Guan & Dongle Wang & Hu Shao & Hao Fu & Jincheng Zhou, 2023. "Understanding the topology of the road network and identifying key bayonet nodes to avoid traffic congestion," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 34(03), pages 1-22, March.
    16. Sun, Yeran & Mburu, Lucy & Wang, Shaohua, 2016. "Analysis of community properties and node properties to understand the structure of the bus transport network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 523-530.
    17. Feng, Xiao & He, Shiwei & Li, Guangye & Chi, Jushang, 2021. "Transfer network of high-speed rail and aviation: Structure and critical components," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    18. Linyuan Lü & Yi-Cheng Zhang & Chi Ho Yeung & Tao Zhou, 2011. "Leaders in Social Networks, the Delicious Case," PLOS ONE, Public Library of Science, vol. 6(6), pages 1-9, June.
    19. Wang, Shuliang & Chen, Chen & Zhang, Jianhua & Gu, Xifeng & Huang, Xiaodi, 2022. "Vulnerability assessment of urban road traffic systems based on traffic flow," International Journal of Critical Infrastructure Protection, Elsevier, vol. 38(C).
    20. Yang, Yuanzhi & Yu, Lei & Wang, Xing & Zhou, Zhongliang & Chen, You & Kou, Tian, 2019. "A novel method to evaluate node importance in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    21. Li, Tao & Rong, Lili, 2020. "A comprehensive method for the robustness assessment of high-speed rail network with operation data: A case in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 666-681.
    22. Wang, Longjian & Zheng, Shaoya & Wang, Yonggang & Wang, Longfei, 2021. "Identification of critical nodes in multimodal transportation network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    23. 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.
    24. Galvan, Giulio & Agarwal, Jitendra, 2020. "Assessing the vulnerability of infrastructure networks based on distribution measures," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    25. Wang, Ning & Gao, Ying & He, Jia-tao & Yang, Jun, 2022. "Robustness evaluation of the air cargo network considering node importance and attack cost," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    26. Tao, Li & Kong, Shengzhou & He, Langzhou & Zhang, Fan & Li, Xianghua & Jia, Tao & Han, Zhen, 2022. "A sequential-path tree-based centrality for identifying influential spreaders in temporal networks," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    27. Wen, Tao & Jiang, Wen, 2019. "Identifying influential nodes based on fuzzy local dimension in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 119(C), pages 332-342.
    28. Jing, Weiwei & Xu, Xiangdong & Pu, Yichao, 2020. "Route redundancy-based approach to identify the critical stations in metro networks: A mean-excess probability measure," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    29. Zhang, Jianhua & Wang, Ziqi & Wang, Shuliang & Shao, Wenchao & Zhao, Xun & Liu, Weizhi, 2021. "Vulnerability assessments of weighted urban rail transit networks with integrated coupled map lattices," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    30. Manlio De Domenico & Albert Solé-Ribalta & Elisa Omodei & Sergio Gómez & Alex Arenas, 2015. "Ranking in interconnected multilayer networks reveals versatile nodes," Nature Communications, Nature, vol. 6(1), pages 1-6, November.
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