An Adaptive-Margin Support Vector Regression for Short-Term Traffic Flow Forecast

2013 ◽  
Vol 17 (4) ◽  
pp. 317-327 ◽  
Author(s):  
Dali Wei ◽  
Hongchao Liu
ICCTP 2011 ◽  
2011 ◽  
Author(s):  
Feng Chen ◽  
Yuanhua Jia ◽  
Wenjuan An ◽  
Na Zhang ◽  
Zhonghai Niu

2021 ◽  
pp. 2150245
Author(s):  
Xiaoquan Wang ◽  
Wenjun Li ◽  
Chaoying Yin ◽  
Shaoyu Zeng ◽  
Peng Liu

This study proposes a short-term traffic flow prediction approach based on multiple traffic flow basic parameters, in which the chaos theory and support vector regression are utilized. First, a high-dimensional variable space can be obtained according to the traffic flow fundamental function. Then, a maximum conditional entropy method is proposed to determine the embedding dimension. And multiple time series are reconstructed based on the phase space reconstruction theory using the time delay obtained by mutual information method and the embedding dimension captured by the maximum conditional entropy method. Finally, the reconstructed phase space is used as the input and the support vector regression optimized by the genetic algorithm is utilized to predict the traffic flow. Numerical experiments are performed and the results show that the approach proposed has strong fitting capability and better prediction accuracy.


Author(s):  
Zhongzheng Guo ◽  
Weifeng Zhong ◽  
Fenghua Zhu ◽  
Xiaoshuang Li ◽  
Fei-Yue Wang ◽  
...  

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