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Abstract

This study investigates the determinants of traffic resilience and clearance time (CT) during regional hurricane evacuations, addressing the limited empirical understanding of how evacuation efficiency evolves across multiple disasters. Despite extensive simulation-based studies, few have validated evacuation dynamics using large-scale observed traffic data. To bridge this gap, we analyze real evacuation traffic records from Hurricanes Irma (2017) and Ian (2022) in Florida, U.S., and develop a negative exponential decay model to quantify the relationship between CT and lane capacity. Results show that CT decreases at an accelerating rate as lane capacity increases, with higher decay rates indicating more efficient evacuations. Notably, the increase in decay rates over this period suggests an improvement in traffic resilience. Geographic factors play a crucial role in shaping both CT and resilience—regions with higher populations, low road network density, and limited evacuation corridors experience prolonged evacuation times. Expanding corridor capacity proves to be a key strategy in accelerating evacuations, underscoring its significance in strengthening traffic resilience. These findings provide critical insights for policymakers and emergency planners, offering data-driven strategies to optimize evacuation planning and enhance the resilience of transportation systems against hurricanes and other natural disasters.

Keywords

traffic resilience hurricane evacuation clearance time corridor capacity

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Investigating Traffic Resilience and Clearance Time in Hurricane Evacuations: The Role of Population,Geographic Factors,and Evacuation Corridors

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