Efficient Vulnerability Assessment of Large-Scale Dynamic Transportation Networks

Abstract

Static and dynamic methods are used to assess the efficiency and vulnerability of transportation networks. Dynamic methods identify both where and when disruptions would be most detrimental. However, exhaustive analysis is computationally prohibitive for large networks because thousands of simulations are required. To enhance the scalability of dynamic transportation network vulnerability assessment, this article presents a subnetwork approach to identify the impact of an edge closure at a specific time by simulating only a portion of the network immediately surrounding the disruption and then incorporating this subnetwork simulation into the baseline scenario without disruptions. Our experiments confirm a strong correlation between the results of complete network simulation and the subnetwork approach with significant computational reductions. Subnetwork size is a tunable parameter, enabling a tradeoff between the accuracy of the subnetwork approach relative to the exhaustive approach and time savings achieved. We also identify a heuristic method to select the subnetwork size for any network to reap the greatest benefits with respect to accuracy and speed up. The subnetwork vulnerability assessment method is subsequently used to allocate limited resources to mitigate travel time disruptions. The approach only required hours not months to complete, advancing methods for city-scale disaster recovery and planning.