A simulation-based framework for leveraging shared autonomous vehicles to enhance disaster evacuations in rural regions with a focus on vulnerable populations

Abstract Rural areas face distinct challenges during disaster evacuations, such as lower income levels, reduced risk perception, longer travel distances, and vulnerabilities of residents. Traditional evacuation methods, which often rely on state-owned buses and city-owned vans, frequently fall short...

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Main Authors: Alican Sevim, Qianwen Guo, Eren Erman Ozguven
Format: Article
Language:English
Published: SpringerOpen 2025-03-01
Series:Journal of Infrastructure Preservation and Resilience
Subjects:
Online Access:https://doi.org/10.1186/s43065-025-00122-6
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author Alican Sevim
Qianwen Guo
Eren Erman Ozguven
author_facet Alican Sevim
Qianwen Guo
Eren Erman Ozguven
author_sort Alican Sevim
collection DOAJ
description Abstract Rural areas face distinct challenges during disaster evacuations, such as lower income levels, reduced risk perception, longer travel distances, and vulnerabilities of residents. Traditional evacuation methods, which often rely on state-owned buses and city-owned vans, frequently fall short of meeting the public’s needs. However, 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. Additionally, a comparison experiment was conducted to examine whether the observed improvements with SAVs were a result of their unique capabilities or simply coincidental, by replacing SAVs with conventional buses under identical conditions.
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spelling doaj-art-1f5b7ae93c594dfda775e6b3a4a43c1d2025-08-20T03:06:00ZengSpringerOpenJournal of Infrastructure Preservation and Resilience2662-25212025-03-016113110.1186/s43065-025-00122-6A simulation-based framework for leveraging shared autonomous vehicles to enhance disaster evacuations in rural regions with a focus on vulnerable populationsAlican Sevim0Qianwen Guo1Eren Erman Ozguven2Department of Civil and Environmental Engineering, Florida Agricultural & Mechanical University-Florida State University College of EngineeringDepartment of Civil and Environmental Engineering, Florida Agricultural & Mechanical University-Florida State University College of EngineeringDepartment of Civil and Environmental Engineering, Florida Agricultural & Mechanical University-Florida State University College of EngineeringAbstract Rural areas face distinct challenges during disaster evacuations, such as lower income levels, reduced risk perception, longer travel distances, and vulnerabilities of residents. Traditional evacuation methods, which often rely on state-owned buses and city-owned vans, frequently fall short of meeting the public’s needs. However, 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. Additionally, a comparison experiment was conducted to examine whether the observed improvements with SAVs were a result of their unique capabilities or simply coincidental, by replacing SAVs with conventional buses under identical conditions.https://doi.org/10.1186/s43065-025-00122-6Rural areasShared autonomous vehicles (SAVs)SUMODisaster evacuationsVulnerable populations
spellingShingle Alican Sevim
Qianwen Guo
Eren Erman Ozguven
A simulation-based framework for leveraging shared autonomous vehicles to enhance disaster evacuations in rural regions with a focus on vulnerable populations
Journal of Infrastructure Preservation and Resilience
Rural areas
Shared autonomous vehicles (SAVs)
SUMO
Disaster evacuations
Vulnerable populations
title A simulation-based framework for leveraging shared autonomous vehicles to enhance disaster evacuations in rural regions with a focus on vulnerable populations
title_full A simulation-based framework for leveraging shared autonomous vehicles to enhance disaster evacuations in rural regions with a focus on vulnerable populations
title_fullStr A simulation-based framework for leveraging shared autonomous vehicles to enhance disaster evacuations in rural regions with a focus on vulnerable populations
title_full_unstemmed A simulation-based framework for leveraging shared autonomous vehicles to enhance disaster evacuations in rural regions with a focus on vulnerable populations
title_short A simulation-based framework for leveraging shared autonomous vehicles to enhance disaster evacuations in rural regions with a focus on vulnerable populations
title_sort simulation based framework for leveraging shared autonomous vehicles to enhance disaster evacuations in rural regions with a focus on vulnerable populations
topic Rural areas
Shared autonomous vehicles (SAVs)
SUMO
Disaster evacuations
Vulnerable populations
url https://doi.org/10.1186/s43065-025-00122-6
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