Evacuation destination choices during Hurricane Ian: A direct demand modeling approach
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911597539098624 |
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| author | Recalde, Alessandra Liu, Luyu Zhang, Xiaojian Park, Sangung Jiang, Shangkun Zhao, Xilei |
| author_facet | Recalde, Alessandra Liu, Luyu Zhang, Xiaojian Park, Sangung Jiang, Shangkun Zhao, Xilei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_14657 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Evacuation destination choices during Hurricane Ian: A direct demand modeling approach Recalde, Alessandra Liu, Luyu Zhang, Xiaojian Park, Sangung Jiang, Shangkun Zhao, Xilei Applications 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. |
| title | Evacuation destination choices during Hurricane Ian: A direct demand modeling approach |
| topic | Applications |
| url | https://arxiv.org/abs/2604.14657 |