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Hurricane evacuation has become a major problem for the coastal residents ofthe United States. Devastating hurricanes have threatened the lives andinfrastructure of coastal communities and caused billions of dollars in damage.There is a need for better traffic management strategies to improve the safetyand mobility of evacuation traffic. In this study hurricane evacuation trafficwas simulated using SUMO a microscopic traffic simulation model. The effects ofConnected and Autonomous Vehicles (CAVs) and Autonomous Vehicles (AVs) wereevaluated using two approaches. (i) Using the state-of-the-art car-followingmodels available in SUMO and (ii) a co-simulation study by integrating themicroscopic traffic simulation model with a separate communication simulator tofind the realistic effect of CAVs on evacuation traffic. A road network of I-75in Florida was created to represent real-world evacuation traffic observed inHurricane Irma s evacuation periods. Simulation experiments were performed bycreating mixed traffic scenarios with 25, 50, 75 and 100 percentages ofdifferent vehicle technologies including CAVs or AVs and human-driven vehicles.HDV Simulation results suggest that the CACC car-following model, implementedin SUMO and commonly used in the literature to represent CAVs, produces highlyunstable results On the other hand the ACC car following model, used torepresent AVs, produces better and more stable results. However, in aco-simulation study, to evaluate the effect of CAVs in the same evacuationtraffic scenario, results indicate that with 25 percentage of CAVs the numberof potential collisions decrease up to 42.5 percentage.

A Co-Simulation Study to Assess the Impacts of Connected and Autonomous Vehicles on Traffic Flow Stability during Hurricane Evacuation