Intelligent Traffic Signal Optimization for Automated Emergency Vehicles Using Machine Learning

Effective management of traffic for emergency vehicles continues to pose a significant challenge in contemporary urban environments. Delays in emergency responses can result in dire outcomes, such as loss of life and damage to property. Conventional traffic light systems function on predetermined cycles and do not adjust dynamically to the requirements of emergencies. This paper introduces an intelligent model for traffic signal optimization that utilizes machine learning techniques to give precedence to automated emergency vehicles. The system evaluates real-time traffic data, forecasts vehicle movement, and creates adaptive green corridors specifically for emergency vehicles. Simulation outcomes indicate a reduction in response times and minimal interference with overall traffic flow.