Published Paper Details:

Urban congestion presents one of the biggest problems to the emergency response system, especially in areas where hospitals are accessed by ambulances through complex routes, where congestion can delay reaching the hospital. Conventional strategies for traffic signal preemption and optimal routes use genetic algorithms and A* search, which are static methods and therefore not amenable to real-time traffic changes. This report explores an advanced approach to addressing congestion in ambulance traffic that uses machine learning. The approach makes a continuous consideration of optimizing route selection and signal timings dynamically. It integrates real-time data from IoT sensors, GPS, and vehicle-to-infrastructure (V2I) communication, enabling proactive decisions based on predictive models of traffic patterns, thereby adjusting the route and signal timings. The system utilizes machine learning models, reinforcement learning, and predictive analytics to continuously improve traffic management and reduce delays while optimizing emergency response times. This approach works through real-time adaptive control to optimize ambulance passage and the overall traffic flow that leads to quicker arrival times, reduces congestion, and promotes public safety. This paper focuses on how machine learning could help morph the urban traffic management systems and comes up with a scalable and efficient solution to the perennial urban problem, which paves the way for smarter, data-driven solutions in emergency services and urban mobility.