Traffic Control System using Deep Q-Network with Replay Memory

Urban road congestion presents significant challenges, causing economic losses, environmental damage, and a lower quality of life. Conventional traffic management systems often struggle to handle unpredictable traffic patterns, leading to delays and inefficient road use. This paper introduces an adaptive traffic management system that leverages real-time data analysis through advanced image processing and artificial intelligence (AI). The system adapts traffic signals in real-time based on how traffic is flowing, helping vehicles move more smoothly and giving priority to the busiest lanes. By integrating machine learning (ML), the system learns from historical and live data, making proactive signal adjustments to prevent congestion. This intelligent coordination reduces wait times, enhances intersection throughput, and ensures balanced vehicle distribution across lanes. In addition to improving traffic flow, the system lowers fuel consumption and greenhouse gas emissions by reducing vehicle idling, supporting environmental sustainability. The adaptive model is highly scalable, making it suitable for cities of different sizes and complexities. It can also integrate with other smart city technologies, such as connected vehicles and public transit systems, to enhance urban mobility. The proposed system offers a sustainable and efficient solution for addressing the growing challenges of urban transportation networks.