Journal of Hebei University(Natural Science Edition) >

2017 , Vol. 37 >Issue 3: 302 - 308

DOI: https://doi.org/10.3969/j.issn.1000-1565.2017.03.013

A forecasting method of highway traffic flow using LSSVM optimized by ADPSO algorithm

  • SI Wenjing ,
  • FENG Xibo ,
  • GENG Liyan ,
  • ZHANG Zhanfu
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  • 1.Construction Engineering Department, North China Institute of Aerospace Engineering, Langfang 065000, China; 2.Hebei Province Expressway Langfang Beisanxian CountyManagement Department, Langfang 065000, China; 3.School of Economics and Management, Shijiazhuang Tiedao University, Shijiazhuang 050043, China; 4.Business School, ManchesterMetropolitan University, Manchester, M15 6BH, UK; 5.Sifang College, Shijiazhuang TiedaoUniversity, Shijiazhuang 051132, China

Online published: 2017-05-25

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Abstract

There is a complex nonlinear relationship between highway traffic flow and its influencing factors.Combing least squares support vector machines(LSSVM)with adaptive dynamic particle swarm- optimization(ADPSO)algorithm,this paper proposed a new highway traffic flow forecasting method based on LSSVM optimized by ADPSO algorithm.Highway traffic flow was forecasted by LSSVM with the advantages of easy modeling and high precision.And the optimal parameters of LSSVM were selected based on the good optimization ability of ADPSO algorithm.An example analysis on the highway traffic flow in a city was performed to test the effectiveness of LSSVM-ADPSO model.The results indicate that the proposed method has better highway traffic flow forecasting performance and is suitable for short-term highway traffic flow forecasting.

Key words： highway; traffic flow forecasting; adaptive dynamic particle swarm optimization algorithm; least squares support vector machines

Cite this article

SI Wenjing , FENG Xibo , GENG Liyan , ZHANG Zhanfu . A forecasting method of highway traffic flow using LSSVM optimized by ADPSO algorithm[J]. Journal of Hebei University(Natural Science Edition), 2017 , 37(3) : 302 -308 . DOI: 10.3969/j.issn.1000-1565.2017.03.013

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