Traffic Route Prediction System using Machine Learning

The Traffic Route Prediction project addresses urban congestion by utilizing machine learning algorithms and real-time analytics to predict and optimize routes.As traditional methods fail to adapt dynamically, this system integrates historical data, real-time conditions, and environmental factors using advanced algorithms such as Support Vector Machines (SVM), Decision Trees, and Random Forests.Real-time data from GPS and traffic sensors enhances prediction accuracy, enabling adaptive route recommendations.The system provides commuters and authorities with a user-friendly interface displaying dynamic navigation suggestions and proactively identifies congestion points, continuously adjusting routes based on live conditions.This predictive capability significantly reduces travel time, congestion, fuel consumption, and emissions.Although technological dependency and data accuracy present challenges, the predictive approach substantially improves transportation efficiency.The implementation demonstrates Decision Trees and Random Forest models outperforming SVM, achieving accuracies over 90%.Ultimately, the Traffic Route Prediction system revolutionizes urban mobility by delivering proactive, accurate, and adaptable solutions, significantly enhancing commuter satisfaction and urban living quality.