Resiliency of Intelligent Transportation Systems to Critical Disruptions: An Eigenvalue-Based Viewpoint

Identification of important nodes/links is critical for understanding the resiliency of transportation networks in response to disruptions. Envisioning an intelligent transportation system requires effective monitoring of different components of a transportation network, such as nodes and links. For example, identification of critical nodes/links for placing different monitoring devices (e.g., cameras) is an important task of an intelligent transportation system. This paper is focused on identifying important nodes of a given transportation network based on a novel approach that minimizes the largest eigen value of an adjacency matrix of a transportation network. We tested the proposed approach on the Guam road network. Our results suggested that if capacities of 15% of the critical nodes are reduced by 50%, the connectivity of the entire network drops by 50%, which is significant performance degradation. The proposed approach is more effective than existing popular metrics (e.g., betweenness) in identifying a subset of critical nodes/links.