Optimizing Emergency Response Efficiency in Urban Road Networks: A Data-Driven Approach for Fire Station Placement and Resource Allocation

As road transport continues to evolve with advancements in automation and intelligent traffic management, optimizing emergency response operations remains a critical challenge in urban mobility. This study presents an innovative data-driven framework for optimizing fire station placement in Birjand, Iran, integrating transportation efficiency with emergency service accessibility. A binary integer programming model was developed to minimize response time and transportation costs while incorporating real-world constraints. Using dynamic simulations in MATLAB 2019b, the study analyzed existing fire station coverage across seven urban regions, assessing travel efficiency based on an average vehicle speed of 52.5 km/h and a 5 min response threshold. Key findings highlight disparities in emergency service accessibility, with high-demand areas such as R4 and R5 lacking sufficient coverage, while low-demand regions like R6 remain underserved. To address this, a genetic algorithm (GA) with 100 individuals over 20 generations was implemented. Optimizing total penalized response time, calculated as the objective value of GA, is 25.89 min. This value represents the sum of penalized response times across all station-area assignments. A cost–benefit analysis revealed Station 2 as the most efficient investment, achieving a net benefit of 3163 million IRR at a 1% discount rate, outperforming Station 1 (2831 million IRR). Sensitivity analysis confirmed Station 2’s financial advantage across discount rates up to 10%. This research contributes to emerging transportation challenges by bridging emergency response optimization with urban mobility strategies. The proposed decision support system (DSS) integrates adaptive planning, data-driven analytics, and infrastructure investment to enhance resilience and response efficiency in dynamic urban environments.