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 the 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. The superiority of the new hybrid model and the effectiveness of data pre-treatment are demonstrated through the comparison between the prediction results of the DES, autoregressive integrated moving average (ARIMA) and the DES-SVM model.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: pp 130-135
  • Monograph Title: 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

Subject/Index Terms

Filing Info

  • Accession Number: 01568673
  • Record Type: Publication
  • ISBN: 9781479929146
  • Files: TRIS
  • Created Date: May 5 2015 10:57AM