Collaborative Research: Household Response to Wildfire – Integrating Behavioral Science and Evacuation Modeling to Improve Community Wildfire Resilience

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The past four decades have seen a significant increase in wildfire frequency, magnitude, and resulting human and economic losses as driven by climate change and rapid population growth into the wildland-urban interface (WUI, where homes and infrastructure meet the wildland). Wildfire risk can be significantly reduced by three types of pre-event household hazard adjustments—mitigation, preparedness to stay, defend, and survive, and readiness to evacuate. However, the factors that influence the adoption of these hazard adjustments remain poorly understood. Existing wildfire evacuation models rarely consider fire spread dynamics; lack trilateral integration of people, fire hazard, and traffic components; and are based on limited social-behavioral data. To address these knowledge gaps, this project will integrate behavioral data with active learning and goal-setting techniques for increasing WUI residents’ adoption of pre-event hazard adjustments. In addition, social-behavioral data will be infused into transportation engineering models to create more accurate and actionable agent-based models (ABMs) for evacuation. To achieve these objectives, the researchers will collaborate with four WUI communities in three states to (1) identify factors influencing households’ pre-event hazard adjustment adoption and evacuation decision-making for wildfire hazard, and (2) integrate social-behavioral data into wildfire evacuation scenarios using ABMs to evaluate alternative evacuation strategies. Regional planners and emergency managers will be engaged to test and evaluate evacuation protocols and educational programs. This project will expand and strengthen the capability of the Protection Action Decision Model (PADM) to explain complex decision-making processes related to wildfire mitigation, stay/defend/survive preparedness, and evacuation readiness. Specifically, this project’s results will advance our knowledge in pre-event risk messaging about wildfire hazards and address the urgent need for incorporating multidimensional datasets in wildfire evacuation models. The study of four different WUI communities will allow assessment of the cross-population generalizability within and beyond the project’s study areas. The outcomes will lead to best practices for motivating households’ protective actions, assessments of community-informed evidence-based strategies for wildfire evacuation modeling, testing of alternative wildfire warning messaging strategies, and, ultimately, reduction in wildfire risk to residents and businesses. The diverse project team includes early-career scientists, students, and researchers from underrepresented groups. The project will build upon previous collaborations with community stakeholders to co-produce and share knowledge throughout the research process, ensuring that the work will promote data-driven policies and resource allocation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.