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A hypothesis-driven framework for resilience analysis of public transport network under compound failure scenarios

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  • Zhang, Yifan
  • Ng, S. Thomas

Abstract

Urban flood risk is becoming a severe issue in most cities and has been a critical global concern of the twenty-first century. Flooding events can trigger significant failures in public transport networks (PTNs) within urban areas. With a growing population in urban areas, demand for transportation increases. Consequently, the dual forces of climate change and rapid urbanization have intensified the size and frequency of floods. Furthermore, the increasing threat of targeted attacks, natural hazards, and compound failures to PTNs has called for an urgent need to improve the resilience of critical lifeline systems. Here, we propose a new synthetic criterion for identifying the critical nodes of PTNs. Additionally, a hypothesis-driven framework for resilience analysis of PTNs against deliberate and compound failures is developed, which we demonstrate on Mass Transit Railway (MTR) in Hong Kong to obtain a quantitative understanding of resilience. Besides, the quantitative comparison of the viability of multiple recovery strategies suggests that the proposed method (EWM−TOPSIS) performs the best for the MTR network to recover after a disruption. Our findings can generalize across urban critical infrastructure systems as well as natural and human systems.

Suggested Citation

  • Zhang, Yifan & Ng, S. Thomas, 2021. "A hypothesis-driven framework for resilience analysis of public transport network under compound failure scenarios," International Journal of Critical Infrastructure Protection, Elsevier, vol. 35(C).
    • Handle: RePEc:eee:ijocip:v:35:y:2021:i:c:s1874548221000470
    DOI: 10.1016/j.ijcip.2021.100455
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    Cited by:

    1. Oster, Matthew R. & Amburg, Ilya & Chatterjee, Samrat & Eisenberg, Daniel A. & Thomas, Dennis G. & Pan, Feng & Ganguly, Auroop R., 2024. "A tri-level optimization model for interdependent infrastructure network resilience against compound hazard events," International Journal of Critical Infrastructure Protection, Elsevier, vol. 47(C).
    2. Liping Ge & Stefan Voß & Lin Xie, 2022. "Robustness and disturbances in public transport," Public Transport, Springer, vol. 14(1), pages 191-261, March.
    3. Zhang, Yuan & Li, Yifan & Tian, Zhen & Yang, Chao & Peng, Hao & Kan, Ankang & Gao, Wenzhong, 2025. "Thermodynamic performance prediction and optimization of a 1 kW ocean thermal energy cogeneration system based on artificial neural network," Energy, Elsevier, vol. 314(C).
    4. Zhang, Yifan & Ng, S. Thomas, 2022. "Robustness of urban railway networks against the cascading failures induced by the fluctuation of passenger flow," Reliability Engineering and System Safety, Elsevier, vol. 219(C).

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