Optimizing Wildfire Evacuations through Scenario-Based Simulations with Autonomous Vehicles

Abstract

Natural disasters like hurricanes, wildfires, and floods pose immediate hazards. Such events often necessitate prompt emergency evacuations to save lives and reduce fatalities, injuries, and property damage. This study focuses on optimizing wildfire evacuations by analyzing the influence of different transportation infrastructures and the penetration of autonomous vehicles (AVs) on a historical wildfire event. The methodology involves modeling various evacuation scenarios and incorporating different intersection traffic controls such as roundabouts and stop signs and an evacuation strategy like lane reversal with various AV penetration rates. The analysis results demonstrate that specific interventions on evacuation routes can significantly reduce travel times during evacuations. Additionally, a comparative analysis across different scenarios shows a promising improvement in travel time with a higher level of AV penetration. These findings advocate for the integration of autonomous technologies as a crucial component of future emergency response strategies, demonstrating the potential for broader applications in disaster management. Future studies can expand on these findings by examining the broader implications of integrating AVs in emergency evacuations.