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Fast-moving dire wildfire evacuation simulation
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AI
DOI：10.1016/j.trd.2022.103190.png)
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Abstract
En 中文
Extreme wildfire events are fires that move too fast or erratically for adequate evacuation or notification. Modeling evacuations from these wildfires is markedly different than modeling disasters with slower or more predictable movements. There is a gap in our knowledge of human evacuation behavior under these conditions. In this paper, we develop an agent-based simulation model (ABM) of the 2018 Camp Fire evacuation in Northern California, an extreme wildfire evacuation. We use a post-disaster survey and decision tree methods to model agent movements, empirically grounding evacuee behavior. We explore individual scenarios inspired by the Camp Fire, simulating limited smartphone access, delays in awareness time, and reduced vehicle access. We then combine these to produce worst case critical scenarios. Our results show that longer evacuation travel times are associated with reduced smartphone use, increased delays in awareness, and reduced vehicle access. More agents are trapped with awareness delays and limited vehicles.
Keywords:
Agent-Based Modeling
Evacuation
Wildfire
Wildland Urban Interface (WUI)
Natural disaster
Journal
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4.4K
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2.5W
