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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jinjun Tang Guangning Xu Yinhai Wang Hua Wang Shen Zhang Fang Liu |
| Copyright Year | 2013 |
| Description | Author affiliation: Dept. of Energy & Traffic Eng., Inner Mongolia Agric. Univ., Hohhot, China (Fang Liu) || Dept. of Civil & Environ. Eng., Univ. of Washington, Seattle, WA, USA (Yinhai Wang) || Dept. of Transp. Sci. & Eng., Harbin Inst. of Technol., Harbin, China (Jinjun Tang; Guangning Xu; Hua Wang; Shen Zhang) |
| Abstract | 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. |
| Sponsorship | IEEE Intell.Transp. Syst. Soc. |
| Starting Page | 130 |
| Ending Page | 135 |
| File Size | 693431 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479929146 |
| DOI | 10.1109/ITSC.2013.6728222 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-10-06 |
| Publisher Place | Netherlands |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Predictive models Data models Smoothing methods Support vector machines Solid modeling Optimization |
| Content Type | Text |
| Resource Type | Article |
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