Intelligent Traffic Signal Optimization Using Deep Reinforcement Learning

Intelligent traffic management has become a critical requirement in modern urban environments due to the rapid increase in vehicle population and traffic congestion. Traditional traffic signal control systems operate on fixed timing schedules and often fail to adapt to dynamic traffic conditions, resulting in increased delays, fuel consumption, and environmental pollution. This study presents an intelligent traffic signal optimization system using Deep Reinforcement Learning (DRL) to improve traffic flow efficiency in real-time.

The proposed framework employs a DRL agent that interacts with a simulated traffic environment to learn optimal traffic signal policies based on current traffic density, queue length, and waiting time. By continuously observing traffic conditions and receiving reward feedback, the agent dynamically adjusts signal timings to minimize congestion and maximize traffic throughput. The system integrates deep neural networks with reinforcement learning techniques to handle complex and large-scale traffic scenarios effectively.