Estimating wildfire evacuation decision and departure timing using large-scale GPS data

Recently, wildfires have created severe challenges for fire and emergency services and communities in the wildland-urban interface (WUI). To reduce wildfire risk and enhance the safety of WUI communities, improving our understanding of wildfire evacuation is a pressing need. This study proposes a new methodology to analyze wildfire evacuation by leveraging a largescale GPS dataset. This methodology includes a proxy-home-location inference algorithm and an evacuation-behavior inference algorithm, to systematically identify different groups of wildfire evacuees (i.e., self-evacuee, shadow evacuee, evacuee under warning, and ordered evacuee). We applied the methodology to the 2019 Kincade Fire in Sonoma County, CA. We found that among all groups of evacuees, self-evacuees and shadow evacuees accounted for more than half of the evacuees during the Kincade Fire. The findings of this study can be used by emergency managers and transportation planners to better prepare WUI households for future wildfire events.

Automated summary

This study proposes a new methodology to analyze wildfire evacuation using a largescale GPS dataset, and found that self-evacuees and shadow evacuees accounted for a large proportion of evacuees during the wildfire evacuation. These findings can help emergency managers and transportation planners better prepare WUI communities for future wildfire events.