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A novel method to assess urban multimodal transportation system resilience considering passenger demand and infrastructure supply

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  • Wang, Nanxi
  • Wu, Min
  • Yuen, Kum Fai

Abstract

With the continuous construction of urban infrastructure, urban multi-modal transportation system (UMTS) has gradually become complex and intertwined, which makes the system more uncertain and vulnerable, especially under disruptions. Improving the resilience of UMTS is critical to maintaining urban reliability and sustainability. This paper aims to build effective models to assess the resilience of UMTS. To develop the resilience assessment model, we provide a comprehensive, dynamic index that considers passenger demand and infrastructure supply. Three transportation modes are considered: bus, subway and taxi, and a cascading failure model is simulated. Data from Singapore are collected to analyse resilience indicators’ characteristics and explore relationship between resilience indicators and node measures. The results indicate that 1) when stations experience failure, it has the greatest impact on the accessibility of the transportation network, followed by efficiency and connectivity; 2) system resilience is negatively related to the node degree and node betweenness centrality of the attacked station; 3) beyond a certain threshold, continuing to increase stations’ additional capacity does not significantly increase system resilience. The proposed resilience index can be used as an optimization goal during system design and recovery. The conclusions and recommendations can inform the management and improvement of urban transportation system resilience.

Suggested Citation

  • Wang, Nanxi & Wu, Min & Yuen, Kum Fai, 2023. "A novel method to assess urban multimodal transportation system resilience considering passenger demand and infrastructure supply," Reliability Engineering and System Safety, Elsevier, vol. 238(C).
    • Handle: RePEc:eee:reensy:v:238:y:2023:i:c:s0951832023003927
    DOI: 10.1016/j.ress.2023.109478
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    Cited by:

    1. Sun, Qin & Li, Hongxu & Zhong, Yuanfu & Ren, Kezhou & Zhang, Yingchao, 2024. "Deep reinforcement learning-based resilience enhancement strategy of unmanned weapon system-of-systems under inevitable interferences," Reliability Engineering and System Safety, Elsevier, vol. 242(C).

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