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May 7, 2026Reliability Engineering & System Safety1 citationsOpen Access

Resilience analysis of multi-modal public transport networks under rainstorm waterlogging disasters: A case study in Chengdu, China

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JCJinqu ChenGuangdong Police CollegeXLXiaowei LiuChaowei Group (China)FLFusheng LiKunming University of Science and Technology

Key Points

  • This research aims to develop a model to assess the resilience of multi-modal public transport networks against rainstorm waterlogging disasters.
  • Developed a resilience analysis model for multi-modal transport networks considering waterlogging effects.
  • Conducted numerical experiments on Chengdu's bus-rail transport services.
  • Performed sensitivity analysis focusing on urban road drainage capacity.
  • The resilience metric correlates with total rainfall and its temporal distribution.
  • Dynamic propagation of disruptions significantly affects network resilience.
  • Urban road drainage capacity is identified as the key factor influencing resilience.

Abstract

• A resilience analysis model is proposed for MPT networks under waterlogging. • Waterlogging negative impact and its dynamic propagation process are considered. • The resilience of the bus-rail MPT network in Chengdu is analyzed. Rainstorm waterlogging disasters (RWDs) can severely disrupt the operation of multimodal public transport networks, highlighting the practical significance of assessing their resilience under RWDs. This study develops a resilience analysis model that incorporates the dynamic propagation (DP) of RWD-induced disruptions within a multi-modal public transport (MPT) network. The formulated model is validated through comprehensive numerical experiments on Chengdu’s MPT network consisting of bus and rail services. Results show that the resilience metric is relevant to the total rainfall and its temporal distribution. Meanwhile, the negative effects arising from the DP process must be considered in the network resilience analysis. Sensitivity analysis indicates that urban road drainage capacity is the dominant factor influencing the network resilience. Finally, several practical implications are proposed to enhance the MPT network resilience under RWDs.

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Cite This Study

Chen et al. (2026) studied this question.

synapsesocial.com/papers/69fc2ba98b49bacb8b3479achttps://doi.org/10.1016/j.ress.2026.112827
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