发表机构
University of San Francisco; Accenture（旧金山大学; 埃森哲）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对美国2600万英语能力有限人群的野火疏散信息不均问题，开发多语言智能体系统BEACON，结合XGBoost等技术提供个性化疏散引导
AI 中文摘要
与20世纪70年代相比，当前野火季节已延长了84天，对人们的财务状况及短期和长期健康造成巨大威胁。火灾发生期间，公共机构会发送紧急信息以提供警告和指令。尽管美国有2600万人英语能力有限，但超过80%的紧急信息仅以英语发布，这会导致信息分配和认知的不均衡。为在紧急情况下更好地服务边缘社区，作者开发了BEACON这一服务，提供全面且个性化的疏散引导，包括导航路线、个性化清单以及用户所用语言的聊天机器人。当前系统从Watch Duty获取火灾边界信息、疏散令状态和避难所信息等数据。当用户距离火灾一定范围内时，系统会利用实时GPS位置和美国国家海洋和大气管理局（NOAA）的附近天气数据来预测火灾危险等级。评估模型的更新根据火灾进展和趋势使用XGBoost动态安排。若位置存在火灾危险可能性，系统会发送警报，并提供由避多边形路由管道输出的疏散路线。该应用提供一个上下文感知的多语言智能体，用户可与其交流，且与应用的其他功能紧密相连。此外，基于用户输入的数据，系统会动态生成并勾选个性化提醒项，以提供有条理的疏散计划。应用的用户界面会根据用户最近在设置或聊天机器人对话中使用的语言，为所有应用元素动态更改语言设置。
英文摘要
Wildfire seasons have become 84 days longer in the current days than in the 1970s, causing enormous threats to one's financial status and short- and long-term health. During the fire, public agencies send out emergency messages to provide warnings and orders. Although 26 million people in the US have limited English proficiency, over 80% of those messages are only delivered in English, which can cause disproportionate information distribution and awareness. In order to better serve marginalized communities during emergencies, the authors developed BEACON, a service that provides comprehensive and personalized evacuation guidance, including navigation routes, personalized checklists, and a chatbot in the language that a user uses. Our current system ingests data including fire perimeter information, evacuation order status, and shelter information from Watch Duty. When a user is within a certain proximity from the fire, the system utilizes real-time GPS locations and nearby weather data from the National Oceanic and Atmospheric Administration (NOAA) to predict fire danger levels. The assessment model refreshment are dynamically scheduled based on fire progress and trends using XGBoost. If the location has a likelihood of fire danger, the system sends alerts with evacuation routes outputted from a polygon-avoidant routing pipeline. The application provides a context-aware multilingual agent that users can communicate with and is tightly connected to other features of the application. In addition, based on data that the user entered, the system dynamically generates and checks off personalized reminder items to provide an organized evacuation plan. The system's user interface dynamically changes its language settings based on the language the user most recently used in either setting or chatbot conversation for all the application elements.
CommentsThis one is under submission (IEEE SpatialConnect Workshop2026)
