Vehicle Density Prediction from Historical Traffic Dataset using Machine Learning Techniques

Traffic congestion is a serious problem for urban areas around the world, negatively impacting the economy and quality of life while increasing carbon emissions. Accurate prediction of vehicle density plays a crucial role in the development of Intelligent Transportation Systems (ITS), as accurate vehicle density predictions are essential for proactively managing traffic, enabling adaptive traffic signal control, and providing real-time route recommendations for vehicles. This study compares the performance of six different Machine Learning (ML) models in vehicle density prediction based on historical spatiotemporal datasets. The selected models are Linear Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Regressor (SVR), Long Short-Term Memory (LSTM), and Bidirectional Long Short- Term Memory (BiLSTM). The historical dataset contains vehicle density measured by hourly vehicle counts, as well as relevant features such as date, weekday, and journey hour (Peak hour and off-peak hour), which are used to conduct a comparative analysis of each machine learning model. The trained models are evaluated based on Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and coefficient of determination (R2 Score). The results demonstrate the potential of machine learning in improving the accuracy of vehicle density predictions. XGBoost, among the six models, achieves the highest predictive accuracy.