Enhancing Emergency Response: A Smart Ambulance System Using Game-Building Theory and Real-Time Optimization

Abstract

Dispatching ambulances early and efficiently is paramount and difficult in the field of emergency medical services. In this regard, the paper designs a smart ambulance system based on game-building theory. The system employs an advanced Negamax algorithm for optimizing the dispatch of ambulances during emergencies. Besides traditional methods, real-time traffic data, patient condition severity, and dynamic resource allocation also improve the system further. With the integration of predictive analytics and real-time data, it allows dynamic adaptation to changing urban conditions, optimal resource allocation as well as minimizing response time. According to our simulations involving extensive scenarios, our Negamax-based system performs significantly better with respect to average response times when compared with traditional methods averagely reducing them by more than 50%, hence, showing double improvement. The study not only improves efficiency in the operation of emergency services but also presents an expandable framework that can be used for future developments in critical response systems thereby leading to their association with smart city infrastructure and AI-based predictive emergency management.