Abstract:

In order to enhance the resilience of urban rail transit networks to ensure stable operations and passenger safety in the face of emergencies, hypergraph theory is introduced to construct a hypergraph based urban rail transit hypernetwork model, and a nonlinear load-capacity cascading failure model based on passenger flow weighting is established. In response to the passenger evacuation process at actual transportation network stations, a load redistribution mechanism is proposed, taking into consideration both the network level and the importance of passenger flow. To address scenarios where stations in actual traffic networks can still accommodate loads during shutdowns, a node status determination model is designed. The results indicate that the hypergraph allocation mechanism is more consistent with the actual propagation of traffic flow. In terms of network cascading failure, the centrality attack has a greater impact. Moreover, appropriately increasing the node capacity parameter significantly enhances the robustness of the network. The variation in overload capacity coefficient does not influence the occurrence of cascading failures.

Key words: hypernetworks, cascading failures, rail transit, robustness, complex network