Advanced Machine Learning Approaches for Improving Traffic Flow Predictions in Smart Transportation Systems

Traffic flow extrapolation is a vital aspect of intellectual transportation systems, as it facilitates the smooth and efficient management of traffic. However, traditional traffic flow prediction methods have several limitations, including a lack of accuracy and the inability to handle large and complex data. In this study, advanced machine learning techniques are suggested for enhancing the precision and efficiency of traffic flow predictions in intelligent transportation systems. We collect and preprocess a large and diverse dataset of historical traffic flow data, and use techniques such as feature engineering, model selection, model evaluation, model tuning, and model ensemble to develop a robust-based traffic flow prediction system. The system suggested in this research integrates KNN, Random Forest, and ARIMA machine learning algorithms to forecast traffic flow considering various factors including weather, road conditions, and traffic volume. The outcomes show that the projected system significantly enhances the accuracy of traffic flow predictions compared to traditional methods and can handle a wide range of traffic scenarios and conditions. This research demonstrates the potential of advanced machine learning approaches for improving traffic flow predictions in smart transportation systems.