Hybrid Prediction Approach Based on Weekly Similarities of Traffic Flow for Different Temporal Scales

Traffic flow prediction is considered a key technology of intelligent transportation systems. This paper presents a hybrid model that combines double exponential smoothing (DES) and a support vector machine (SVM) to predict traffic flow patterns on the basis of weekly similarities in traffic flow. First, in the hybrid model, DES is applied to predict the future data, and its smoothing parameters are determined by the Levenberg-Marquardt algorithm. Second, the SVM is employed to estimate the residual series between the prediction results by the DES model and actual measured data. In the SVM model, the cross-correlation rule is used to optimize its parameters. Third, a case study to test the proposed model with the data at different temporal scales is presented. Furthermore, data-smoothing strategies, including difference and ratio schemes based on weekly similarities, are applied as data processes before prediction. The proposed hybrid model along with the processing scheme demonstrates superiority in prediction accuracy compared with autoregressive integrated moving average, DES, and DES-SVM models.