Potential of using SAVs in evacuating vulnerable population during tornado early warning: a case study of Tuscaloosa County, Alabama

Riffat Islam, Steven Jones, Jun Liu, Emmanuel Kofi Adanu and Xinwu Qian

Transportation Planning and Technology, 2025, vol. 48, issue 6, 1245-1267

Abstract: This study explores using self-driving shared autonomous vehicles (SAVs) to evacuate vulnerable population without personal transportation during tornado warnings in Tuscaloosa County, Alabama. It simulates a dynamic ridesharing operation based on three historic tornado polygons, testing three SAV capacities (4, 6, and 12 seats) with a one-person-to-one-seat ratio. The results indicate that 4-passenger SAVs are most efficient for populations with lower shelter access. All capacities can evacuate the majority of those with better shelter access within the early warning period. However, none of the fleet sizes managed to evacuate the entire study population within the tornado's early warning time. The study then determines the fleet sizes needed to evacuate the remaining populations in each of the three polygons excluding out-of-range population, located far from the nearest shelter. The findings underscore the potential of SAVs in tornado evacuations and offer valuable insights for emergency management, transportation planning, and policy-making.

Date: 2025
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DOI: 10.1080/03081060.2024.2445646

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