Urban road networks are often affected by natural disasters, such as rainstorms, with consequences spreading from partial failures to massive network-wide disruptions. The pattern of road network resilience is grasped by capturing the changes in the failure process of the road network cascade. This research presents a method for dynamic evaluation of road network resilience, with extreme rainstorm weather as the background. The fundamental diagram of traffic flow is adopted into the shock wave model to obtain the congestion propagation and dissipation model with speed as input quantity. Then, it is networked and applied to the road network to develop a cascading failure model. The model can be used to identify critical nodes, combining the Monte Carlo method and Dijkstra algorithm, as well as to construct time-varying evolution scenarios of road network resilience when critical nodes are broken. Furthermore, VA (the variations of all OD pairs in the road network) and RI (the resilience indicators of road network) indexes for dynamic evaluation of road network resilience are developed, that visualize the resilience of the road network for a specific region, a specific event, and a specific time. In the case study, the proposed method is employed to evaluate the resilience of the urban road network under a real case of the Zhengzhou 720 rainstorm in China, and its practicality is verified.
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