Geospatial Analysis for Emergency Services: Mapping Accident Hotspots to Enhance Ambulance Accessibility

A huge number of deaths and injuries caused by car accidents is one of the most urgent problems we will face today in the world. Ambulances can be pre-positioned to shorten response time and give immediate medical assistance instead of being dispatched just when needed. When it comes to healthcare decision-making and problem-solving, deep learning approaches have already shown to be indispensable. In order to forecast the best places to position ambulances, this research presents a method based on deep-embedded clustering. It is critical to understand these linkages while creating models because road crash occurrence is heavily influenced by various regional characteristics and patterns. This study uses CAT2VEC, another model of deep learning, to implement the need to maintain such formulas in the entire model building to ensure real -time results. Traditional clustering techniques such as K-Means, GMM and agglomerative clustering are also compared to the proposed framework. For the purpose of evaluating the efficiency of various algorithms, a new feature of scoring to calculate the time and distance of the response in real time has been developed. The proposed ambulance system acts impressively exceeds all other conventional methods, reaches a speed of accuracy of 95% with cross validation of K-time and a unique score of 7.581 distance.