Optimizing Real-Time Traffic Control Through Image Processing and Machine Learning

Urbanization and rapid increases in vehicular den- sity have led to persistent challenges in managing city traffic, with static signal systems often proving inadequate. This paper presents a comprehensive study on optimizing real-time traffic control using image processing and machine learning techniques. We first conduct a chronological literature review, analyzing the evolution of adaptive traffic control systems and highlighting the incremental advances that have addressed previous limita- tions. Drawing on recent innovations, our proposed methodol- ogy implements a multi-camera, imageprocessing-based traffic density estimator combined with deep reinforcement learning (DRL) for signal optimization. The approach integrates edge- detection, background subtraction, and advanced thresholding to generate accurate vehicle counts, which are fed into a DRL controller. This framework is evaluated against several state-of- the-art benchmarks. Quantitative results demonstrate significant improvements in metrics such as average queue length, waiting time, and throughput compared to classical and contemporary methods. The paper provides detailed mathematical modeling and comparative analysis. Our findings establish that image processing coupled with machine learning, particularly DRL, enables scalable, adaptive, and robust urban traffic management, offering substantial benefits in congestion reduction, travel time, and environmental sustainability.