TRAFFIC PREDICTION FOR INTELLIGENCE TRANSPORT SYSTEM USING MACHINE LEARNING

This research introduces a Machine Learning-based Traffic Prediction and Signal Optimization System designed for Intelligent Transportation Systems (ITS). The system utilizes real-time traffic data captured via cameras to predict future traffic conditions. It employs data preprocessing and feature engineering, followed by the application of various machine learning models. These models are trained on historical and camera-captured data, evaluated for accuracy, and deployed for real-time traffic prediction. The system not only provides traffic forecasts but also enhances traffic signal control, optimizing stops and flow. This holistic approach aids in traffic management, congestion reduction, and informed decision-making within transportation networks, ultimately enhancing the efficiency of urban mobility.