Road transport resilience under extreme rainfall: Integrating multiple impact factors and delay propagation

Extreme rainfall significantly affects road transport networks through flood, reduced visibility, and traffic signal failures. However, existing research does not integrate all three of these impact factors, and triggered delay propagation across the network under rainfall conditions is not thoroughly captured in resilience assessments. This study achieves this by embedding these impacts within a dynamic traffic assignment framework based on the link transmission model, which enables a detailed representation of network-wide delay propagation under rainfall conditions. Network performance is evaluated using the average delay per time slice, and a dimensionless, delay-based resilience index is defined as the ratio of cumulative delay under normal conditions to that under an extreme-rainfall scenario. A case study of the large-scale road network in Greater London and its surrounding cities reveals that spillover delays (delay increase outside the rainfall-affected areas) constitute 0.42, 0.34, and 0.27 of the total delay increase for the 1-in-30, 1-in-100, and 1-in-1000-year rainfall scenarios, respectively. The corresponding mean resilience values are 0.79, 0.66, and 0.59, which means that cumulative delays under these scenarios are approximately 27%, 52%, and 69% higher than under normal conditions. Moreover, attributes of the rainfall-affected areas—such as traffic demand, road length, area size and high-risk flooded segment length —demonstrate significant correlation with network resilience, as indicated by the good model fit observed under the 1-in-30-year rainfall scenarios. The proposed framework captures the spatio-temporal evolution of delays and diagnoses congestion hotspots under extreme rainfall, thereby providing decision support for traffic management and emergency response in urban road networks