Vulnerability modeling and assessment in urban transit systems considering disaster chains: A weighted complex network approach

Abstract

This research develops a hybrid approach that integrates disaster chains and complex networks to model and assess vulnerability in urban transit systems. A weighted complex network is established by combining various disaster chains and their frequencies in the operation of the urban transit system, where disaster events and their transmission paths in chains are considered as nodes and edges in the disaster network, respectively. Several measures are proposed to quantify the vulnerability magnitude of the disaster nodes, edges, and network from both micro-level and macro-level perspectives. A weighted complex network consisting of 31 disaster nodes and 73 edges is constructed to model the disaster transmission in the operation of the metro system. Results show that (1) The steward misoperation (Node 15), the unqualified maintenance (Node 17), and the edge E38 (from Node 17 to Node 23) are identified as the most vulnerable nodes and edge, which should be paid more attention for disaster risk reduction; (2) The network efficiency is calculated to be 0.1244, acting as an anchor for improving system resilience against disasters; (3) The proposed edge vulnerability measure proves to be more effective in discovering more vulnerable edges than the traditional edge measure. Both node and edge attack analysis are performed to discuss the robustness of the developed complex network. It is found that all the intentional attack strategies display better performance for reducing network efficiency than random attack strategies, and global attack strategies are more efficient in both node and edge attack analysis than local attack strategies. This indicates that the developed approach is necessarily effective to identify the vulnerability of disaster events and links in the urban transit system, enabling to discover the best strategy for improving the resilience of the transit system against disasters.