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Modelling the resilience of rail passenger transport networks affected by large-scale disruptive events: the case of HSR (high speed rail)

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  • Milan Janić

    (Delft University of Technology
    Delft University of Technology)

Abstract

This paper deals with modelling the dynamic resilience of rail passenger transport networks affected by large-scale disruptive events whose impacts deteriorate the networks’ planned infrastructural, operational, economic, and social-economic performances represented by the selected indicators. The indicators of infrastructural performances refer to the physical and operational conditions of the networks’ lines and stations, and supportive facilities and equipment. Those of the operational performances include transport services scheduled along particular routes, their seating capacity, and corresponding transport work/capacity. The indicators of economic performances include the costs of cancelled and long-delayed transport services imposed on the main actors/stakeholder involved—the rail operator(s) and users/passengers. The indicators of social-economic performances reflect the compromised accessibility and consequent prevention of the user/passenger trips and their contribution to the local/regional/national Gross Domestic Product. Modeling resulted in developing a methodology including two sets of analytical models for: (1) assessing the dynamic resilience of a given rail network, i.e., before, during, and after the impacts of disruptive event(s); and (2) estimation of the indicators of particular performances as the figures-of-merit for assessing the network’s resilience under the given conditions. As such, the methodology could be used for estimating the resilience of different topologies of rail passenger networks affected by past, current, and future disruptive events, the latest according to the “what-if” scenario approach and after introducing the appropriate assumptions. The methodology has been applied to a past case—the Japanese Shinkansen HSR network affected by a large-scale disruptive event—the Great East Japan Earthquake on 11 March 2011.

Suggested Citation

  • Milan Janić, 2018. "Modelling the resilience of rail passenger transport networks affected by large-scale disruptive events: the case of HSR (high speed rail)," Transportation, Springer, vol. 45(4), pages 1101-1137, July.
  • Handle: RePEc:kap:transp:v:45:y:2018:i:4:d:10.1007_s11116-018-9875-6
    DOI: 10.1007/s11116-018-9875-6
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    Cited by:

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    3. Das, Laya & Munikoti, Sai & Natarajan, Balasubramaniam & Srinivasan, Babji, 2020. "Measuring smart grid resilience: Methods, challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    4. Knoester, Max J. & Bešinović, Nikola & Afghari, Amir Pooyan & Goverde, Rob M.P. & van Egmond, Jochen, 2024. "A data-driven approach for quantifying the resilience of railway networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
    5. Adrian J. Hickford & Simon P. Blainey & Alejandro Ortega Hortelano & Raghav Pant, 2018. "Resilience engineering: theory and practice in interdependent infrastructure systems," Environment Systems and Decisions, Springer, vol. 38(3), pages 278-291, September.
    6. 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.
    7. Liu, Aijun & Li, Zengxian & Shang, Wen-Long & Ochieng, Washington, 2023. "Performance evaluation model of transportation infrastructure: Perspective of COVID-19," Transportation Research Part A: Policy and Practice, Elsevier, vol. 170(C).
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    9. 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.
    10. 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).

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