Traffic flow prediction based on hybrid model using double exponential smoothing and support vector machine

This study develops a hybrid model that combines double exponential smoothing (DES) and support vector machine (SVM) to implement a traffic flow predictor. In the hybrid model, DES is used firstly to predict the future data, and the smoothing parameters of the DES are determined by Levenberg-Marquardt algorithm. Then, SVM is employed to fit the residual series between the predicting results of DES model and actual measured data for its powerful no-linear fitting ability. Finally, a practical application is used to testify the proposed model. In the application, data smoothing and wavelet de-noising technology are applied as data pre-treatment before prediction. In addition, the data smoothing contains difference and ratio smoothing strategy. It is demonstrated the superiority of the new hybrid model and the effectiveness of data pre-treatment through the comparison between the prediction results of DES, autoregressive integrated moving average (ARIMA) and DES-SVM model.