Ensemble Learning-based Algorithms for Traffic Flow Prediction in Smart Traffic Systems

Abstract

Due to the tremendous growth of road traffic accidents, Intelligent Transportation Systems (ITSs) are becoming even more important. To prevent road traffic accidents in the long term, it is necessary to find new vehicle flow management techniques in order to optimize traffic flow. With the high growth of deep learning and machine learning, these methods are increasingly being used in ITSs. This research provides a novel conceptual ITS model that aims to predict vehicle movement through the collective learning usage to anticipate intersections. The proposed approach consists of three main stages: data collection through cameras and sensors, implementation of machine learning and deep learning algorithms, and result evaluation, utilizing the coefficient of determination (R-squared), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). To accomplish this, various machine learning and deep learning algorithms, such as Random Forest, LSTM, Linear Regression, and ensemble methods (bagging), were incorporated into the model. The findings revealed the enhancement due to the proposed method, which was observed through a significant performance improvement of 93.52%.