{"id":16092,"date":"2025-05-28T09:18:43","date_gmt":"2025-05-28T13:18:43","guid":{"rendered":"https:\/\/www.rochester.edu\/coe\/?p=16092"},"modified":"2025-05-28T10:25:25","modified_gmt":"2025-05-28T14:25:25","slug":"city-of-rochester-enhancing-emergency-response-efficiency-for-the-rochester-fire-department","status":"publish","type":"post","link":"https:\/\/www.rochester.edu\/coe\/city-of-rochester-enhancing-emergency-response-efficiency-for-the-rochester-fire-department\/","title":{"rendered":"Enhancing Emergency Response Efficiency for the Rochester Fire Department"},"content":{"rendered":"

\"Data<\/p>\n

Team Members: Brynn Lee, Eugene Ayonga, Homayra Tabassum, Medhini Sridharr, Nour Assili<\/em><\/p>\n

The Rochester Fire Department (RFD) serves over 211,000 residents and responds to
\n40,000 incident calls each year. This project aimed to improve RFD\u2019s operational efficiency
\nby analyzing historical incident data and forecasting future trends to optimize resource
\nallocation. Using advanced geospatial analysis and predictive modeling, students identified key patterns in incidents and evaluated the allocation of personnel and equipment across 15
\nfire stations. The team utilized included time-series forecasting with the Prophet model and developed interactive maps to visualize response times and incident types. The model achieved strong predictive accuracy, capturing seasonal and spatial trends, and provided actionable insights for resource reallocation. Results included recommendations for relocating units to high-demand areas and implementing a low-acuity response program to reduce the strain on emergency resources. These strategies will position RFD to meet growing demands in their jurisdiction while maintaining timely and equitable service.<\/p>\n","protected":false},"excerpt":{"rendered":"

Team Members: Brynn Lee, Eugene Ayonga, Homayra Tabassum, Medhini Sridharr, Nour Assili The Rochester Fire Department (RFD) serves over 211,000 residents and responds to 40,000 incident calls each year. This…<\/p>\n","protected":false},"author":162,"featured_media":16112,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[42],"tags":[72,62],"class_list":["post-16092","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-fall-2024-projects","tag-city-of-rochester","tag-public-sector"],"yoast_head":"\nEnhancing Emergency Response Efficiency for the Rochester Fire Department - Center of Excellence in Data Science and Artificial Intelligence<\/title>\n<meta name=\"description\" content=\"The team analyzed fire department incident data and used time-series forecasting (Prophet) and geospatial analysis to improve emergency response resource allocation. 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