INTELLIGENT TRAFFIC MANAGEMENT FOR VEHICULAR NETWORKS USING MACHINE LEARNING

As urbanization and vehicular density continue to rise, the efficient management of traffic in vehicular networks becomes increasingly critical. This paper presents an innovative approach to intelligent traffic management leveraging Machine Learning (ML) techniques, specifically employing Support Vector Machines (SVM) with Radial Basis Function (RBF) kernels. The integration of SVM with RBF proves to be particularly effective in capturing complex non-linear relationships within the dynamic and unpredictable vehicular environment. Our proposed system aims to enhance traffic flow, reduce congestion, and improve overall transportation efficiency. The SVM-RBF model is trained on diverse datasets encompassing various traffic scenarios, considering factors such as vehicle speed, density, and historical traffic patterns. Through continuous learning, the system adapts to real-time changes, making it robust and responsive to dynamic traffic conditions. The core functionality of the intelligent traffic management system involves predicting traffic patterns and optimizing signal timings at intersections. The SVM-RBF model excels in its ability to classify and predict intricate traffic behavior, allowing for proactive decision-making. This proactive approach facilitates the timely adjustment of traffic signals, rerouting strategies, and adaptive speed limit recommendations. The effectiveness of the proposed system is validated through extensive simulations and real-world experiments, demonstrating significant improvements in traffic flow and reduction in travel times. Furthermore, the system exhibits scalability, making it suitable for deployment in diverse urban environments.