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Unveiling network vulnerability under multiple area-covering disruption scenarios: A scenario-enumeration-free model and empirical insights into targeted protection

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  • Yang, Junze
  • Xu, Xiangdong
  • Ryu, Seungkyu

Abstract

Multiple area-covering disruptions in transportation networks refer to events involving simultaneous failure or closure of several areas (e.g., flooding), which can cause broader impacts than link or node disruptions, with effects not simply additive from individual area failures. This study addresses the challenges in analyzing such disruptions, which include handling randomness in their occurrence locations and impact ranges, and dealing with the combinatorial complexity of multiple disruption scenarios. In particular, based on the observed hierarchical state dependencies among disrupted areas, links, paths, origin–destination (OD) pairs, and the overall network, we propose a vulnerability analysis model that obviates the need for brute-force enumeration of all potential disruption scenarios. Methodologically, compared with widely-adopted bi-level programming models for multiple-link disruptions, the proposed model is directly formulated as a single-level mixed-integer linear programming, thereby offering better computational tractability due to its compact model structure. The model also exhibits remarkable flexibility, as it allows specifications of diverse network performance indicators and can model various types of disruption scenarios. Two specific models considering network connectivity and route redundancy are expounded and solved using a customized Benders decomposition method. Case studies involving the Sioux Falls and Winnipeg networks demonstrate the effectiveness and features of the models. Results show that increasing the number of disrupted areas has a diminishing marginal effect on network performance. Moreover, shared critical areas that consistently constitute critical area combinations across various multiple area-covering disruption scenarios warrant targeted protection, as their disruption can increase the risk of extreme combinatorial failure scenarios.

Suggested Citation

  • Yang, Junze & Xu, Xiangdong & Ryu, Seungkyu, 2026. "Unveiling network vulnerability under multiple area-covering disruption scenarios: A scenario-enumeration-free model and empirical insights into targeted protection," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 206(C).
    • Handle: RePEc:eee:transe:v:206:y:2026:i:c:s1366554525005873
    DOI: 10.1016/j.tre.2025.104559
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