Machine Learning Approach to Reducing Urban Congestion Using Artificial Intelligence for Smart Traffic Management

Abstract

With increasing levels of urbanization, urban congestion has become a critical problem that is impacting mobility, productivity, and the environment. The prevalent approach to traffic management depends largely on over-regulated systems which are invariably against static rules. Such methods seem to be inefficient. This work presents a novel approach to an integrated traffic management system based on artificial intelligence, machine learning, IoT, deep learning, computer vision, and reinforcement learning called dynamic urban traffic flow management. This SSP is designed to use real-time traffic data from IoT sensors, GPS, CCTV cameras, and even satellite feeds to predict congestion patterns and algorithmically adaptively control traffic signals. Long short term memory, convolutional neural networks, and deep Q-network neural architectures are used for this cause. The system is focused on creating an AI-driven model of signal control that increases the efficiency of intersections while minimizing congestion and travel delays, as well as carbon emissions. The results illustrate the high potential of modern techniques of traffic control to enhance mobility in cities and contribute to the development of sustainable and efficient transportation systems within smart cities.