Leveraging Spatial and Temporal Data to Predict Heavy Freight Vehicle Traffic Flow on Rural Road Network - Alireza Gholami; Seyedehsan Seyedabrishami (IEEE Access)
Accurately predicting heavy freight vehicle (HFV) traffic flow is essential for optimizing service levels and improving road safety management. This study compares the performance of three machine learning approaches—classical learning, reinforcement learning, and artificial neural networks—in predicting the traffic flow of trucks and tractor-trailers. The dataset consists of hourly traffic records spanning three years and seven months on rural road network segments in southern Iran. The modeling framework integrates temporal and spatial variables along with lagged traffic volumes from upstream counters. Model performance is assessed through an expanding window cross-validation approach, which defines separate training, validation, and testing datasets. The results indicate that while the seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) model performs well on the training data, it exhibits limitations in predicting test data. The extreme gradient boosting (XGBoost) model surpasses the time-series model in predictive accuracy, yielding average R-squared values of 84.7% and 85.8% on the test data for trucks and tractor-trailers, respectively. In comparison, the multi-layer perceptron (MLP) model achieves average R-squared values of 86.8% for trucks and 82.9% for tractor-trailers. In addition to R-squared, model performance has been further evaluated using RMSE and MAE metrics, offering a more nuanced and reliable understanding of prediction accuracy across different error dimensions. Analysis based on SHAP values and sensitivity assessment demonstrates that particular times of day and traffic counter classifications exert the greatest influence on HFV traffic flow predictions. Overall, the MLP model demonstrates superior predictive performance.