A data-driven approach to improving evacuation time estimates for resort communities during wildfires

Keywords: wildfire evacuation, evacuation time estimates, traffic simulation
Session Type: Virtual Paper Abstract
Day: Friday
Session Start / End Time: 2/25/2022 05:20 PM (Eastern Time (US & Canada)) - 2/25/2022 06:40 PM (Eastern Time (US & Canada))
Room: Virtual 67

Abstract

Wildfire has caused significant loss of life and property in the western U.S. in the past few fire seasons. Computerized modeling of wildfire evacuation could help incident commanders improve situational awareness and facilitate protective action decision-making. In this study, we leverage big data, traffic simulation model, and geographic information systems to develop a wildfire evacuation model to improve evacuation time estimates in resort areas. Specifically, we take into account household vehicle ownership data and the occupancy rate of second homes based on a variety of data in model construction. The Tahoe Donner neighborhood in Truckee, CA was used as a case study to demonstrate the use of the proposed method. The results indicate that the evacuation time estimates vary significantly with the mean number of vehicles per home and second homes' occupancy rate in resort areas. The proposed method could help incident commanders better understand the dynamics of travel demand of the fire-prone communities in resort areas during wildfire evacuation.