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Passenger railway network protection: a model with variable post-disruption demand service

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  • Stefano Starita
  • Maria Paola Scaparra

Abstract

Protecting transportation infrastructures is critical to avoid loss of life and to guard against economic upheaval. This paper addresses the problem of identifying optimal protection plans for passenger rail transportation networks, given a limited budget. We propose a bi-level protection model which extends and refines the model previously introduced by Scaparra et al, (Railway infrastructure security, Springer, New York, 2015). In our extension, we still measure the impact of rail disruptions in terms of the amount of unserved passenger demand. However, our model captures the post-disruption user behaviour in a more accurate way by assuming that passenger demand for rail services after disruptions varies with the extent of the travel delays. To solve this complex bi-level model, we develop a simulated annealing algorithm. The efficiency of the heuristic is tested on a set of randomly generated instances and compared with the one of a more standard exact decomposition algorithm. To illustrate how the modelling approach might be used in practice to inform protection planning decisions, we present a case study based on the London Underground. The case study also highlights the importance of capturing flow demand adjustments in response to increased travel time in a mathematical model.

Suggested Citation

  • Stefano Starita & Maria Paola Scaparra, 2018. "Passenger railway network protection: a model with variable post-disruption demand service," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 69(4), pages 603-618, April.
  • Handle: RePEc:taf:tjorxx:v:69:y:2018:i:4:p:603-618
    DOI: 10.1057/s41274-017-0255-y
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    Cited by:

    1. Faramondi, Luca & Oliva, Gabriele & Setola, Roberto, 2020. "Multi-criteria node criticality assessment framework for critical infrastructure networks," International Journal of Critical Infrastructure Protection, Elsevier, vol. 28(C).
    2. Nader Azad & Elkafi Hassini, 2019. "A Benders Decomposition Method for Designing Reliable Supply Chain Networks Accounting for Multimitigation Strategies and Demand Losses," Transportation Science, INFORMS, vol. 53(5), pages 1287-1312, September.
    3. Annunziata Esposito Amideo & Stefano Starita & Maria Paola Scaparra, 2019. "Assessing Protection Strategies for Urban Rail Transit Systems: A Case-Study on the Central London Underground," Sustainability, MDPI, vol. 11(22), pages 1-21, November.
    4. Mingming Zheng & Hanzhang Zuo & Zitong Zhou & Yuhan Bai, 2023. "Recovery Strategies for Urban Rail Transit Network Based on Comprehensive Resilience," Sustainability, MDPI, vol. 15(20), pages 1-17, October.
    5. Girish Ch. Dey & Mamata Jenamani, 2019. "Optimizing fortification plan of capacitated facilities with maximum distance limits," OPSEARCH, Springer;Operational Research Society of India, vol. 56(1), pages 151-173, March.

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