Identifying Critical Nodes in Multimodal Transport Networks Based on Resilience Theory

Identifying critical nodes in a multimodal transport network is fundamental to enhancing the resilience of transport systems against natural or man-made disasters. Although research on identifying critical nodes in complex networks has been deeply accumulated, its practical application in transport systems is still limited and requires further exploration. To bridge this research gap, an improved integrated method for identifying critical nodes in multimodal transport networks is proposed based on resilience theory. This method comprehensively incorporates disaster consequences and recovery time into resilience assessment, and an Improved Binary Particle Swarm Optimization (IBPSO) algorithm is developed to solve this problem efficiently. Then, a real case in China is conducted to verify the effectiveness of the proposed method. The results show that the proposed method can rapidly identify critical nodes in multimodal transport networks from the resilience perspective. These critical nodes, which are mainly concentrated at choke points with long repair durations, are often overlooked in traditional methods. Compared with the five baseline methods, the proposed method improves identification efficiency by more than 20%, verifying its better performance. The research findings can provide a scientific foundation for decisions on the effective protection and rapid recovery of critical nodes in the network.