Ambulance Location Optimization via Fine-grained Emergency Demand Forecasting

Pre-hospital emergency care plays a pivotal role in contemporary emergency medical service systems, as it is paramount for safeguarding the lives and well-being of patients. However, the efficacy of historical emergency resource allocation schemes wanes over time, owing to the impact of urban development and evolving societal dynamics. Consequently, timely adjustments and optimization of emergency resource allocation schemes become imperative to uphold the unblemished efficiency of emergency response. This study delves into the inadequacies of existing optimization endeavors pertaining to pre-hospital emergency resource allocation, encompassing issues such as rudimentary granularity in emergency demand prediction, subpar precision, impractical constraints, and unwarranted costs within the resource optimization process. To surmount these challenges, we propose a sophisticated algorithm for fine-grained emergency resource prediction and a pragmatic and viable algorithm for emergency resource optimization. Empirical findings obtained from an extensive analysis of a pre-hospital emergency dataset from Shenzhen corroborate that our methods diligently optimize the prevailing resource allocation scenario in the city, thereby ensuring a seamless and efficient pre-hospital emergency response.