{"id":57243,"date":"2026-06-12T11:09:23","date_gmt":"2026-06-12T15:09:23","guid":{"rendered":"https:\/\/engineering.jhu.edu\/magazine\/?p=57243"},"modified":"2026-06-12T11:09:24","modified_gmt":"2026-06-12T15:09:24","slug":"tapping-ai-to-improve-wildfire-evacuation","status":"publish","type":"post","link":"https:\/\/engineering.jhu.edu\/magazine\/impact\/tapping-ai-to-improve-wildfire-evacuation\/","title":{"rendered":"Tapping AI to Improve Wildfire Evacuation"},"content":{"rendered":"\n

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. <\/p>\n\n\n\n

\u201cDuring the Los Angeles wildfires last year, we saw a lot of negative effects related to evacuation,\u201d says Susu Xu<\/a>, an assistant professor of civil and systems engineering<\/a>. <\/p>\n\n\n\n

\u201cWhile 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.\u201d <\/p>\n\n\n\n

Xu is leading a Johns Hopkins team that\u2014together with researchers from University of Florida\u2014has been awarded nearly $1.2 million by the National Science Foundation\u2019s 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. <\/p>\n\n\n\n

\u201cBy using simulation models that predict how groups make decisions, we can support more effective planning and evacuation to reduce negative effects,\u201d she says. <\/p>\n\n\n\n

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. <\/p>\n\n\n\n

\n

\u201cBy using simulation models that predict how groups make decisions, we can support more effective planning and evacuation to reduce negative effects.\u201d<\/p>\n\n\n\n

\u2014 Susu Xu<\/p>\n<\/blockquote>\n\n\n\n

\n

Current behavior prediction models include behavioral and psychological theory but don\u2019t 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. <\/p>\n\n\n\n

\u2014 DANIELLE MCKENNA<\/p>\n<\/div>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

A team led by Susu Xu is using AI to predict human behavior and decision-making under stress.<\/p>\n","protected":false},"author":4,"featured_media":57246,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[34],"tags":[],"issue":[105],"class_list":["post-57243","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-impact","issue-spring-2026"],"acf":[],"yoast_head":"\nTapping AI to Improve Wildfire Evacuation - Johns Hopkins Engineering Magazine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/engineering.jhu.edu\/magazine\/impact\/tapping-ai-to-improve-wildfire-evacuation\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Tapping AI to Improve Wildfire Evacuation - 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