A computationally efficient metric for identification of critical links in large transportation networks

Maintaining safety and resilience in transportation system requires identification of critical components. The existing transportation link criticality metrics typically require multiple traffic assignments to perform a full scan of the network. Link removals create computational burden and can cause network disconnectivity which makes it problematic to run traffic assignment. The authors previously introduced Link Criticality Index (LCI) that identifies the criticality ranking within a single User Equilibrium (UE) traffic assignment using Frank-Wolfe (FW) algorithm. While LCI is shown to provide balanced rankings with respect to connectivity and flow conditions, its computational efficiency diminishes for larger networks due to the need for path enumeration. This paper formulates an adjusted LCI to make it computationally efficient for larger networks. Adjusted LCI utilizes 1) viable routes instead of the complete list of enumerated paths; and, 2) a path-based traffic assignment algorithm (Gradient Projection) to replace FW. The consistency of rankings between original/adjusted LCIs, and other measures from the literature are analyzed and compared for three networks, and through an experimental setup. The proposed method is also applied on a large-scale transportation network, i.e., Chicago network. The results show that both modifications are effective and yield faster results without compromising LCI's advantages.