When a wildfire strikes, residents, incident response teams, and public safety officials spring into action to make decisions aimed at preserving life and property. But not all those decisions are sound.
“During the Los Angeles wildfires last year, we saw a lot of negative effects related to evacuation,” says Susu Xu, an assistant professor of civil and systems engineering.
“While some people left prior to the fires, others chose to evacuate at the last minute, causing traffic congestion and prompting drivers to abandon their cars on the same roads used by emergency response teams. We also saw that many people did not have the means to evacuate.”
Xu is leading a Johns Hopkins team that—together with researchers from University of Florida—has been awarded nearly $1.2 million by the National Science Foundation’s Fire Science Innovations Through Research and Education program to advance AI-enabled tools that predict human behavior during wildfire evacuations. Xu says the project aims to strengthen community resilience by understanding how civilians, incident response teams, and public safety officials make protective action decisions during deadly blazes.
“By using simulation models that predict how groups make decisions, we can support more effective planning and evacuation to reduce negative effects,” she says.
The project will produce simulation methods to promote teaching, training, and learning and will support wildfire resilience by allowing public safety officials to use open-access tools.
“By using simulation models that predict how groups make decisions, we can support more effective planning and evacuation to reduce negative effects.”
— Susu Xu
Current behavior prediction models include behavioral and psychological theory but don’t fully represent the complexity of human decision-making. The team found that by introducing behavioral theory and psychological theory to guide the reasoning process of large language models, or LLMs, they could significantly improve mimicry of the human reasoning process, allowing them to better understand how implicit mental states are shaped under stressful conditions, how people are making decisions, and how choices vary among individuals and groups.
— DANIELLE MCKENNA