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Real-time multistep prediction of public parking spaces based on Fourier trans form-least squares support vector regression
| 题名： | Real-time multistep prediction of public parking spaces based on Fourier trans form-least squares support vector regression |
| 正文语种： | 英文 |
| 作者： | Zhenyu Mei;Wei Zhang;Lihui Zhang;Dianhai Wang |
| 作者单位： | Zhejiang University;Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies |
| 关键词： | CO complexity; Fourier transform; LSSVR; multistep prediction; parking spaces |
| 摘要： | Multistep prediction of public parking spaces in the parking guidance and information system and parki ng reservati on system has great ben efits for in telligent parking. This study analyzes the CO complexity of parking space occupancy time series from the frequency domain aspect. Results show that regular components account for the vast majority of park-ing space occupancy time series and can be considered a "quasiperiodic" series, which provides the theoretical basis for multistep prediction. This study combines the idea of Fourier transform (FT) and a machine learning method least squares support vector regression (LSSVR) together and proposes the Fourier transform-least squares support vector regres-sion (FT-LSSVR) multistep prediction algorithm. As taking consideration of a predicting step threshold, this method has the power to predict single-step and multistep public parking spaces. Verification on two typical public parking lots in Hangzhou shows the great perform-ance of FT-LSSVR. The prediction accuracy of proposed FT-LSSVR immensely outperforms the traditional LSSVR prediction after consider!ng the step threshold. Moreover, the proposed method did not add the computational time complexity compared with the traditional LSSVR prediction. Thus, the proposed method is more suitable for real-time systems for its high prediction accuracy and less complex calculation. |
| 出版日期： | 2020 |
| 出版年： | 2020 |
| 期刊名称： | Journal of Intelligent Transportation Systems Technology Planning and Operations |
| 卷： | Vol24 |
| 期： | No01-06 |
| 页码： | 68-80 |
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