A Simulation-Based Framework for Leveraging Shared Autonomous Vehicles to Enhance Disaster Evacuations in Rural Regions with a Focus on Vulnerable Populations
This study addresses accessibility and equity challenges faced by vulnerable rural populations—including persons with disabilities, older adults, and limited-English-proficient individuals—during disaster evacuation. We propose the first two-stage coordinated scheduling framework for shared autonomous vehicles (SAVs) specifically designed for rural contexts. Methodologically, the framework integrates integer linear programming with SUMO-based microscopic traffic simulation and innovatively incorporates dynamic road network resilience modeling—accounting for road closures and capacity degradation—to support both pre-disaster SAV pre-positioning and post-disaster real-time response. Evaluated in Sumter County, Florida, full SAV deployment (100% fleet coverage) reduced peak congestion by 37% and increased average vehicle speed by 22%, while significantly improving evacuation coverage and traffic flow stability. Results demonstrate the framework’s robustness and equity-preserving capabilities under infrastructure degradation, offering a scalable solution for resilient rural emergency mobility.