Journal of South China University of Technology (Natural Science Edition) ›› 2016, Vol. 44 ›› Issue (4): 109-114.doi: 10.3969/j.issn.1000-565X.2016.04.016

• Traffic & Transportation Engineering • Previous Articles     Next Articles

Short-Term Traffic Flow Prediction Based on Phase Space Reconstruction and RELM

SHANG Qiang1 YANG Zhao-sheng1,2,3 LI Zhi-lin1 LI Lin1 QU Xin1   

  1. 1.College of Transportation,Jilin University,Changchun 130022,Jilin,China; 2.State Key Laboratory of Automobile Simulation and Control,Jilin University,Changchun 130022,Jilin,China; 3.Jilin Province Key Laboratory of Road Traffic,Jilin University,Changchun 130022,Jilin,China
  • Received:2015-06-17 Revised:2015-12-11 Online:2016-04-25 Published:2016-04-12
  • Contact: 杨兆升(1938-) ，男，教授，博士生导师，主要从事智能交通系统关键理论与技术研究． E-mail:yangzs@jlu.edu.cn
  • About author:商强(1987-) ，男，博士生，主要从事智能交通信息处理与应用研究． E-mail: shangqiang14@ mails． jlu． edu． cn
  • Supported by:
    Supported by the National Key Technology Reserch and Development Program the Ministry of Science and Technology of China( 2014BAG03B03) and the National Natural Science Foundation of China( 51308249， 51308248， 51408257)

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Abstract: In order to increase the accuracy of short-time traffic flow prediction,a flow prediction model based on the phase space reconstruction and the regularized extreme learning machine is put forward.In this method，the CC method is used to calculate the best time delay and embedding dimension of traffic flow time series for phase space reconstruction,and the G-P algorithm is used to calculate the correlative dimension of the seriesthat is an important judgment index ofthe chaotic characteristics of traffic flow series,Then,the reconstructed phase point data are taken as the inputs and outputsto trainthe regularized extreme learning machine model,and the main parameters of the model are determined by means of grid searching.Finally,a comparative analysis is carried out based on the actual measured traffic flow data.The results show that the proposed model possesses high performance and is effective in improving the accuracy of short-time traffic flow prediction.

Key words: trafficengineering, short-term traffic prediction, phase space method, extreme learning machine

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