Utilization of AI in Traffic Management Systems for Reducing Congestion and Emissions through Real-Time Signal Optimization and Pattern Recognition Models

Abstract

Traffic congestion and vehicular emissions pose significant challenges to urban sustainability and public health. This study explores the application of artificial intelligence (AI) in traffic management systems, focusing on real-time signal optimization and pattern recognition models to mitigate congestion and reduce emissions. Employing a mixed-methods approach, the research integrates real-world traffic data from urban intersections with AI-driven algorithms, including machine learning and deep learning models, to optimize signal timings and predict traffic patterns. Findings indicate that AI-based systems can reduce average delay times by up to 25% and emissions by 18% in high-traffic scenarios. The study highlights the potential of AI to enhance traffic flow efficiency and environmental outcomes while identifying scalability and computational challenges. These results inform urban planners and policymakers on integrating AI into smart city frameworks, emphasizing sustainable traffic management.