Short-term traffic volume prediction using classification and regression trees

Accurate short-term traffic flow prediction plays a fundamental role in intelligent transportation systems (ITS), e.g. advanced traffic management systems (ATMS). To generate accurate short-term traffic volume, nonparametric models have gained credit from quantities of researchers. On the basis of the common thought that future traffic states can be predicted according to the similar states in the historical traffic data, this paper presents a novel nonparametric-model-based method to predict the short-term traffic volume. The applied nonparametric model is the classification and regression trees (CART) model. In the application, the CART model first classifies the historical traffic states into plentiful categories. Afterwards, the linear regression model is built corresponding to each traffic state pattern. Finally, the model predicts the short-term traffic state through clustering the current state vector into the most congenial historical pattern and regression model. In the experiments, the proposed method is tested by using the 15 minutes average traffic volumes on freeways and is compared with the classic nonparametric methods k-nearest neighbors (k-NN) model, and the parametric method Kalman filter model. The results indicate that the CART-based prediction method outperforms the k-NN and Kalman filter methods in both the mean absolute percentage error and the mean absolute scaled error.