FIRE-WUI: A Convergence Framework for Modeling Collective Human Behavior in Wildfire Evacuation

Strengthening wildfire resilience requires accurate modeling and a deep understanding of collective human behavior during wildfire evacuations. In particular, there is a critical need for simulation models that can realistically capture how civilians, incident commanders, and public safety officials make protective action decisions during wildfires. However, existing simulation models face fundamental limitations that often cause low prediction accuracy and insufficient capacity to support effective decision-making during wildfire response. Therefore, this project aims to develop a convergent framework for next-generation wildfire evacuation simulation that features realistic Artificial Intelligence (AI) agents powered by psychological theory-informed large language models (LLMs), reinforcement learning, and multi-modal datasets. This research is a transformative step toward improving the behavioral realism, prediction accuracy, and decision-support capability of wildfire evacuation simulation models. This project will also lead to generalizable simulation methods, promote teaching, training, and learning, strengthen partnerships, and support wildfire resilience through broad dissemination and open-access tools.