Traffic Route Prediction using Machine Learning

Urban-Traffic congestion presents a number of difficulties, such as delays, higher fuel usage, and environmental issues. The goal of this project was to use machine learning algorithms—specifically, Decision Trees, Random Forest, and Support Vector Machines (SVM)—to create a prediction model for traffic routes in order to address these problems. The study made use of openly accessible traffic datasets that included average speeds, vehicle counts, and timestamps. For comparison on non-linear data categorization, SVM was used. The results showed that Random Forest performed better than Decision Trees in terms of accuracy and dependability, while SVM showed strong predictive power with carefully chosen kernel functions, even if it required more computing power. According to the results, ensemble techniques like Random Forest offer the best possible balance between computational economy and accuracy, which makes them appropriate for real-time traffic control systems. To further enhance prediction performance and scalability, future studies should investigate utilizing deep learning models and including real-time traffic updates.