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 traffic controls and the penetration of autonomous vehicles (AVs) on a historical wildfire event. The methodology involves modeling various evacuation scenarios by 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 strongly advocate for the integration of autonomous technologies as a crucial component of future emergency evacuation strategies, and demonstrate the potential for broader applications in disaster management. Future studies can expand on these findings and simulate diverse wildfire scenarios to model traffic dynamics under varying conditions, taking into account factors such as fire spread rates and wind direction. This would allow for a deeper understanding of real-time decision-making in evacuations and the role of AVs in managing congestion. Ultimately, this study advocates for the integration of autonomous technologies in evacuations and emphasize their crucial role in improving public safety and evacuation effectiveness during natural disasters.