Simulation Study on Topology Characteristics and Cascading Failure of Hefei Subway Network

Abstract

The structural characteristics and robustness of subway networks are important for improving the safety and efficiency of subway operations. Based on complex network theory, this study analyzed the structural characteristics of the Hefei subway network and evaluated its robustness after suffering from accidents. Specifically: (1) A model of the Hefei subway network was established using the space-L method, and its topological structural characteristics were quantitatively analyzed; (2) An improved cascading failure simulation model was established, and a node importance evaluation system was developed to identify the critical nodes in the Hefei subway network; (3) A simulation analysis was conducted to evaluate the robustness of the Hefei subway network under different scenarios. The results show that the Hefei subway network is different from a scale-free network and small-world network, and the structure was most severely damaged when facing attacks against critical nodes in the cascading failure scenario. Moreover, as the value of the parameter of the control node capacity in the cascade failure model varied, the degree of damage to the subway network also varied considerably. We believe that the results obtained from the study could provide a reference for the construction and planning of the subway network.

Study on the robustness of Washington Metro Network

“…Yin Xiaohong et al used Space L method to construct the topology of Hefei subway, and simulated the robustness of Hefei subway network in different scenarios. The results show that the Hefei subway network is different from the scale-free network and the small-world network [2]. When the key nodes are attacked under the cascading failure scenario, the structural damage is the most serious.…”

Enhancing the Robustness of Scale-Free Networks: The Simulation of Cascade Failures with Adjustable Initial Load Parameters

“…This assumption aligns with reality, as nodes with larger capacities have a higher likelihood of avoiding overloading failures and should be prioritized to maintain the system's functionality as much as possible. Therefore, when node i failed in the network, we assumed that the failure load would be redistributed to the neighbors, where ∆L j (t) is proportional to the capacity of the neighbors, as shown in Equation (5).…”

“…In the cascading process, the neighbors of a failure node share the load of the failed node. It is known from the introduction in Section 2 that when a node fails, a redistribution of the load can be triggered based on Equation (5). To make the neighbors of the node in the network still available with the lowest cost, we should set a minimal α for the neighbors to bear the failure load.…”

“…Complex networks are abstract models for understanding complex systems in the real world, and they are a valuable framework for understanding the interconnections among various elements in the real world, and research in this field is gaining significant traction among scholars. Numerous complex systems, in reality, can be effectively represented as complex networks [1][2][3][4], including transportation networks [5], power grids [6][7][8], social networks [9][10][11], and many other large-scale real networks. Scale-free networks exhibit a unique characteristic wherein a few hub nodes possess extensive connections, while the majority of these nodes have relatively few connections.…”