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ZHAO Jian-you, ZHOU Sun-feng, CUI Xiao-juan, WANG Gao-qing. Predictive method of highway freight volume based on fuzzy linear regression model[J]. Journal of Traffic and Transportation Engineering, 2012, 12(3): 80-85. doi: 10.19818/j.cnki.1671-1637.2012.03.012
Citation: ZHAO Jian-you, ZHOU Sun-feng, CUI Xiao-juan, WANG Gao-qing. Predictive method of highway freight volume based on fuzzy linear regression model[J]. Journal of Traffic and Transportation Engineering, 2012, 12(3): 80-85. doi: 10.19818/j.cnki.1671-1637.2012.03.012
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Predictive method of highway freight volume based on fuzzy linear regression model

doi: 10.19818/j.cnki.1671-1637.2012.03.012

  • School of Automobile, Chang'an University, Xi'an 710064, Shaanxi, China

More Information
  • Author Bio:

    ZHAO Jian-you (1963-), male, professor, PhD, +86-29-82334371, jyzhao@chd.edu.cn

  • Received Date: 2012-01-09
  • Publish Date: 2012-06-25
    Abstract
  • The influence factors of highway freight volume were determined, such as GDP, population quantity, the total amount of social consuming retails and the output value of sideline products, and a predictive method of highway freight volume based on fuzzy linear regression model was set up.The highway freight hub planning in Yan'an City was taken as an example, the statistical freight volumes from 1995 to 2004 were taken as dependent variables, and the fuzzy coefficients of fuzzy linear regression model were determined.The statistical freight volumes from 2005 to 2010 were taken as verified values, and the goodness of fit for fuzzy linear regression model was analyzed.The predictive results among fuzzy linear regression model, exponential smoothing method, grey model and elastic coefficient method were compared.Analysis result shows that in the fuzzy linear regression model, the average value of t test is 0.673 07, which shows that the difference between predictive value and actual value is not significant, and the prediction effect is better.The average relative errors of four methods are 0.073 1, 0.100 3, 0.167 8, 0.232 9 respectively, so the error of predictive method is smallest.

     

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