Evacuation destination choices during Hurricane Ian: A direct demand modeling approach

2026-04-16

AI Summary

This study addresses limitations in traditional evacuation behavior research, which often relies on survey data susceptible to recall bias and lacks quantitative analysis of social vulnerability’s role in destination choice. Leveraging mobile device location data from Hurricane Ian, the authors integrate the Social Vulnerability Index (SVI) directly into an interzonal direct demand model at the census tract level, jointly accounting for travel impedance (distance) to quantify origin-based evacuation capacity and destination attractiveness. Results reveal that vehicle ownership, group quarters residency, road density, and English proficiency significantly influence evacuation demand, with evacuees exhibiting a clear preference for proximate destinations. This approach mitigates biases inherent in conventional surveys and elucidates the underlying mechanisms linking sociodemographic attributes to spatial evacuation patterns.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Evaluation and Analysis
• Application Domains: Mobility, Driving & Flight

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Hurricanes are causing unprecedented damage to the natural environment, infrastructure, and communities. Understanding evacuation behavior is essential for improving emergency preparedness. Past studies have relied on surveys and interviews, which are prone to recall bias. Additionally, they urge incorporating social vulnerability in evacuation research, emphasizing its impact on evacuation capability and destination choice. This study addresses these gaps by analyzing evacuation behavior using mobile device location data from Hurricane Ian, one of Florida's deadliest hurricanes, and directly incorporating variables from the Social Vulnerability Index (SVI) into a zone-to-zone (census tract level) evacuation demand model. We find that vehicle availability, residence in group quarters, road density, and English proficiency have significant effects on evacuation demand, shaping both the ability to evacuate from origin tracts and the attractiveness of destination tracts. Travel impedance, measured as distance, also plays a significant role, with evacuees substantially less likely to travel longer distances.

Problem

• evacuation behavior
• social vulnerability
• destination choice
• hurricane evacuation
• recall bias

Innovation

• direct demand modeling
• mobile location data
• social vulnerability index
• evacuation destination choice
• hurricane evacuation