An Integrated Framework for Wildfire Evacuation in a Damaged Transportation Network - ICASP14; Ma, Fangjiao; Lee, Ji Yun (My University, 2023)
The severity and duration of wildfires are anticipated to rise as a result of climate change and suppression efforts. As more people are moving to the wildland-urban interface, wildfire activity poses an even greater threat to human lives and properties and challenges emergency preparation. Evacuation is an important way of reducing human losses during a wildfire incident; yet a massive evacuation can increase the traffic burden and congestion significantly. Reduced transportation capacity resulting from wildfire-induced bridge damage may further exacerbate traffic congestion. To account for substantial uncertainties in traffic conditions and human behaviors, wildfire evacuation simulation can be an effective experimental means for emergency management and evacuation planning. In recent years, agent-based modeling (ABM) has received great attention in traffic modeling and evacuation planning due to its ability to capture individual and collective behaviors in a dynamic complex system. While many existing studies applied ABM to community-based transportation evacuation planning during various types of natural hazards, the application of ABM to wildfire evacuation has been much less studied. In addition, most ABM evacuation simulations did not account for the probabilistic nature of incidents, nor did they examine the vulnerability of critical transportation components. This study fills the gap by proposing an integrated framework that incorporates hazard modeling, vulnerability assessment, behavioral analysis, and ABM to simulate wildfire evacuation in a damaged transportation network. The proposed framework consists of four components: wildfire simulation, vulnerability assessment, evacuation model, and traffic simulation. The first component will evaluate the spatiotemporal probability of wildfire occurrence and generate representative wildfire scenarios in probability space. FARSITE will simulate a time-dependent fire-front movement for each scenario and feed that information into the subsequent components. The second component will perform a bridge vulnerability assessment to evaluate wildfire-induced changes in traffic capacity. The third component will construct an evacuee response model based on a stated preference survey to predict individual evacuees’ behavior as a fire-front approaches. The fourth component will simulate traffic conditions by updating traffic demand and capacity at every time step. These four components will allow us to track the movement of all residents and vehicles in a damaged transportation setting. Finally, the proposed framework will be demonstrated with the City of Santa Clarita, California. The framework can be used to predict traffic conditions during an evacuation and identify the critical parts of the transportation network for pre-fire risk mitigation actions aimed at improving mobility during a wildfire evacuation.
14th International Conference on Applications of Statistics and Probability in Civil Engineering(ICASP14)
PUBLISHED