A Simulation-Based Framework for Leveraging Shared Autonomous Vehicles to Enhance Disaster Evacuations in Rural Regions with a Focus on Vulnerable Populations

TL;DR

This study introduces a simulation framework combining mathematical programming and SUMO to evaluate how shared autonomous vehicles can improve disaster evacuations in rural areas, especially for vulnerable populations, by analyzing various deployment scenarios.

Contribution

It presents a novel simulation-based framework integrating traffic modeling and optimization to assess SAV deployment strategies in rural disaster evacuations, focusing on vulnerable groups.

Findings

Higher SAV penetration reduces congestion and improves traffic flow.

Exclusive SAV use leads to higher speeds and stability.

Mixed traffic environments cause reduced average speeds.

Abstract

Rapid advancements in autonomous vehicles (AVs) are poised to revolutionize transportation and communities, including disaster evacuations, particularly through the deployment of Shared Autonomous Vehicles (SAVs). Despite the potential, the use of SAVs in rural disaster evacuations remains an underexplored area. To address this gap, this study proposes a simulation-based framework that integrates both mathematical programming and SUMO traffic simulation to deploy SAVs in pre- and post-disaster evacuations in rural areas. The framework prioritizes the needs of vulnerable groups, including individuals with disabilities, limited English proficiency, and elderly residents. Sumter County, Florida, serves as the case study due to its unique characteristics: a high concentration of vulnerable individuals and limited access to public transportation, making it one of the most transportation-insecure counties in the state. These conditions present significant challenges for evacuation planning in the region. To explore potential solutions, we conducted mass evacuation simulations by incorporating SAVs across seven scenarios. These scenarios represented varying SAV penetration levels, ranging from 20% to 100% of the vulnerable population, and were compared to a baseline scenario using only passenger cars. Additionally, we examined both pre-disaster and post-disaster conditions, accounting for infrastructure failures and road closures. According to the simulation results, higher SAV integration significantly improves traffic distribution and reduces congestion. Scenarios featuring more SAVs exhibited lower congestion peaks and more stable traffic flow. Conversely, mixed traffic environments demonstrate reduced average speeds attributable to interactions between SAVs and passenger cars, while exclusive use of SAVs results in higher speeds and more stable travel patterns.