Stacking Ensemble Learning Process to Predict Rural Road Traffic Flow

Abstract

By predicting and informing the future of traffic through intelligent transportation systems, there is more readiness to avoid traffic congestion. In this study, an ensemble learning process is proposed to predict the hourly traffic flow. First, three base models, including K-nearest neighbors, random forest, and recurrent neural network, are trained. Predictions of base models are given to the XGBoost stacking model and bagged average to determine the final prediction. Two groups of models predict traffic flow of short-term and mid-term future. In mid-term models, predictor features are cyclical temporal features, holidays, and weather conditions. In short-term models, in addition to the mentioned features, the observed traffic flow in the past 3 to 8 hours has been used. The results show that for both short-term and mid-term models, the least prediction error is obtained by the XGBoost model. In mid-term models, the root mean square error of the XGBoost for the Saveh to Tehran direction and Tehran to Saveh direction is 521 and 607 (veh/hr), respectively. For short-term models, these values are decreased to 453 and 386 (veh/hr). This model also brings less prediction error for predicting the first and fourth quartiles of the observed traffic flow as rare events.