吉林大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (6): 1792-1798.doi: 10.13229/j.cnki.jdxbgxb201606006

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Short-term traffic flow prediction based on singular spectrum analysis and CKF-LSSVM

SHANG Qiang1, YANG Zhao-sheng1, 2, 3, ZHANG Wei1, 2, 4, Bing Qi-chun1, ZHOU Xi-yang1   

  1. 1.College of Transportation, Jilin University,Changchun 130022,China;
    2.State Key Laboratory of Automotive Simulation and Control,Jilin University,Changchun 130022, China;
    3.Jilin Province Key Laboratory of Road Traffic,Jilin University, Changchun 130022,China;
    4.Shandong High-speed Co.,Ltd.,Ji'nan 250014,China
  • Received:2015-11-23 Online:2016-11-20 Published:2016-11-20

Abstract: In order to improve the accuracy of short-time traffic flow prediction, a short-term traffic flow prediction model based on singular spectrum analysis and Combined Kernel Function (CKF) Least Square Support Vector Machine (LSSVM) is proposed. Singular spectrum analysis technology is used to filter out the noise of traffic time series. Then, the processed traffic flow data are used to train the CKF-LSSVM, and the parameters of the model are determined by particle swarm optimization algorithm. Finally, validation and comparative analysis of the model are carried out using the measured in Xiamen, China. Experimental results indicate that the proposed model has good prediction performance and can effectively improve the accuracy of short-time traffic flow prediction.

Key words: engineering of communication and transportation system, short-term traffic flow prediction, singular spectrum analysis, support vector machine, combined kernel function

CLC Number: 

  • U491
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