Artificial Intelligence-Based Optimization of Dynamic Traffic Control

Abstract

Traditional traffic systems often depend on predetermined timings and rules or basic fixed techniques, which may lead to congestion and inefficient road utilization during peak traffic times. By utilizing machine learning algorithms which get real-time data sourced from sensors. The proposed system evaluates the traffic density, vehicles type, emergency audio detect and pedestrian activity adjust the signal timings in flexible way. The system uses the deep reinforcement learning to enhance and improve the signal timing, reducing waiting times and optimizing traffic flow.The notable main features include the priority of vehicles, the reduction of idle times, and the improvement of pedestrian safety through dynamic signal timing and adjustments.The results show the integrating AI methodologies into traffic system to promote smart, efficient, and sustainable urban transportation solutions. This research provides the development of intelligent transportation systems that dynamically adapt to changing traffic conditions, improving the experience of passengers.