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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2012  >  12(4): 114-119
FAN Na, ZHAO Xiang-mo, DAI Ming, AN Yi-sheng. Short-term traffic flow prediction model[J]. Journal of Traffic and Transportation Engineering, 2012, 12(4): 114-119. doi: 10.19818/j.cnki.1671-1637.2012.04.015
Citation: FAN Na, ZHAO Xiang-mo, DAI Ming, AN Yi-sheng. Short-term traffic flow prediction model[J]. Journal of Traffic and Transportation Engineering, 2012, 12(4): 114-119. doi: 10.19818/j.cnki.1671-1637.2012.04.015
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Short-term traffic flow prediction model

doi: 10.19818/j.cnki.1671-1637.2012.04.015
  • 1.

    School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

  • 2.

    China Transportation Telecommunication and Information Center, Beijing 100011, China

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  • Author Bio:

    FAN Na (1978-), female, lecturer, PhD, +86-29-82334763, fnsea@163.com

  • Received Date: 2012-02-07
  • Publish Date: 2012-08-25
    Abstract
  • A new hybrid prediction model including two single models of nonparametric regression model and BP neural network model was proposed according to the periodicity and randomness properties of short-term traffic flow.Relevant historical traffic flow data were used in nonparametric regression model to make the prediction result abtained from the databases matching proceeding fully illustrate the cyclical stability of traffic flow.Three-tier BP neural network model was used to reflect the dynamic and nonlinear characters of traffic flow.Fuzzy control algorithm was adopted to get the weight coefficient of each model.New mixed model was constituted by the two single models according to different weight coefficients.The prediction effect of hybrid prediction model was verified by the traffic flow data in 30 d from a certain section in Xi'an.Experimental result indicates that the average relative error of mixed model is 1.26%, and its maximum relative error is 3.53%, so the prediction accuracy of mixed model is obviously higher than two single models, and can accurately reflect the real situation of traffic flow.

     

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