Machine Learning-Driven Smart Traffic Management Using IoT Sensors

Abstract

Urban traffic congestion is a growing challenge, and machine learning combined with IoT is offering data-driven solutions for optimizing traffic flow. This paper explores ML models such as deep reinforcement learning, Bayesian networks, and predictive analytics for smart traffic management. It discusses challenges like data sparsity, sensor reliability, and computational efficiency. Case studies highlight implementations of ML-driven traffic systems, showcasing reduced congestion, improved fuel efficiency, and enhanced urban mobility.