Resilience Modeling of Surface Transportation System in Mixed Traffic Environment

Large-scale natural disasters challenge the resilience of surface transportation system. The objective of this research was to develop a resilience model of surface transportation system in mixed-traffic environment considering varying Connected and Automated Vehicle (CAV) penetration scenarios. As deployment of CAVs are expected to improve traffic operations, a resilience model was developed in this research to evaluate the resilience performance of a transportation system with several CAV penetration levels (0%, 25%, 50%, 75% and 100%) for a given budget and recovery time. The proposed resilience quantification model was applied on a roadway network considering several disaster scenarios. The network capacity in terms of trips at any phase of disaster was compared to the pre-disaster trips to determine the system resilience. The capacity variation and the travel time variation was also estimated. The analysis showed that the resilience phenomenon of the transportation system improved with CAVs in respect of travel time and capacity improvement. The rate of improvement in link travel time for varied CAV penetration was almost identical for different disaster scenarios. For each disaster scenario, the individual link travel time reduced significantly with increased CAV penetration. However, higher penetration of CAVs (i.e., 50% or more), increased the recovery budget requirement. For example, the recovery budget needed for medium and large-scale disasters were 50% and 90% higher respectively compared to the recovery budget needed for a small-scale disaster. These higher costs were primarily needed for repair and replacement of intelligent infrastructure required for CAV