THE SMART ROUTE: MACHINE LEARNING FOR PROACTIVE TRAFFIC MANAGEMENT

Traffic management is highly significant for reducing congestion, minimizing travel delays, and maximizing urban mobility. Conventional traffic control systems often adopt reactive strategies that can hardly deal with dynamic and volatile traffic behaviors. In this paper, we explain how to apply machine learning (ML) methods to realize proactive traffic management for real-time forecasting and decision-making. Based on historical traffic data, real-time sensor readings, and deep learning and reinforcement learning, this research explains how predictive analytics can be used to carry out effective traffic signal control, rerouting plans, and congestion relief.

We present several ML algorithms, such as neural networks, decision trees, and gradient boosting, and explain how they can be used to perform traffic prediction and anomaly detection. Secondly, we discuss how to integrate with IoT, edge computing, and cloud platforms to extend data processing capabilities. Based on experiments and case studies, the traffic management system based on ML can effectively reduce road efficiency problems, reduce pollution, and maximize commuting convenience. This research further explains how AI-based intelligent smart traffic management can help make transportation more sustainable and wiser.

Keywords- Traffic Management, Machine Learning, Predictive Analytics, Smart Cities, Deep Learning, Reinforcement Learning, Traffic Forecasting, Anomaly Detection, IoT, Edge Computing, Cloud Computing, Intelligent Transportation Systems, Congestion Relief

- Traffic Management Machine Learning Predictive Analytics Smart Cities Deep Learning Reinforcement Learning Traffic Forecasting Anomaly Detection IoT Edge Computing Cloud Computing Intelligent Transportation Systems Congestion Relief