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.


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    Title :

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


    Contributors:
    Jinjun Tang (author) / Guangning Xu (author) / Yinhai Wang (author) / Hua Wang (author) / Shen Zhang (author) / Fang Liu (author)


    Publication date :

    2013-10-01


    Size :

    693431 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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