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ZHAO Huai-xin, SUN Xing-xing, XU Qian-qian, HU Yuan-jiao, SUN Chao-yun, LI Wei. Analysis of relavent factors for highway freight volume and freight turnover based on grey entropy method[J]. Journal of Traffic and Transportation Engineering, 2018, 18(4): 160-170. doi: 10.19818/j.cnki.1671-1637.2018.04.017
Citation: ZHAO Huai-xin, SUN Xing-xing, XU Qian-qian, HU Yuan-jiao, SUN Chao-yun, LI Wei. Analysis of relavent factors for highway freight volume and freight turnover based on grey entropy method[J]. Journal of Traffic and Transportation Engineering, 2018, 18(4): 160-170. doi: 10.19818/j.cnki.1671-1637.2018.04.017
shu

Analysis of relavent factors for highway freight volume and freight turnover based on grey entropy method

doi: 10.19818/j.cnki.1671-1637.2018.04.017
  • 1.

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

  • 2.

    Department of Transport of Shaanxi Province, Xi'an 710075, Shaanxi, China

More Information
  • Author Bio:

    ZHAO Huai-xin(1975-), male, professor, doctoral student, zhaohxin@vip.sina.com

    SUN Zhao-yun(1962-), female, professor, PhD, zhaoyunsun@126.com

  • Received Date: 2018-05-21
  • Publish Date: 2018-08-25
    Abstract
  • The interaction effects between the national macro-economic factors and highway freight volume and freight turnover were analyzed. A grey entropy relational degree algorithm based on the grey relational degree algorithm and the entropy weight method was proposed. According to the economic data and highway freight volume and freight turnover data of Shaanxi Statistical Yearbookin the past 14 years, the relevant coefficients between the national macroeconomic factors and highway freight volume and freight turnover were studied, and the influencedegree of each economic indicator on highway freight volume and freight turnover was obtained. By removing the dimensional effect between the data, the relevant coefficients between the economic indicators and highway freight volume and freight turnover were calculated by the grey relational degree algorithm, and the weights of the economic indicators were calculated using the entropy weight method. The relevant degrees between the economic indicators and highway freight volume and freight turnover were calculated based on the relevant coefficients of the economic indicators and their weights. Moreover, the influencing factors of the highway freight volumes in Beijing and Tianjin were analyzed. Analysis result shows that for the economic indicators, the highway freight volume and freight turnover show similar relevant trends. In Shaanxi Province, the relational degrees between the highway freight volume and output value of the primary industry, added value of the industry and output value of the secondary industry are relatively higher, and are 0.944 7, 0.941 7, and 0.940 2, respectively. The relational degrees between the freight turnover and output value of the primary industry, average salary of the onjob employees in the urban units, and per capita GDP are higher, and are 0.920 7, 0.915 9, and 0.915 3, respectively. In Beijing, the relational degrees between the highway freight volume and tertiary industry index, secondary industry index, and per capita GDP index are higher, and are 0.716 2, 0.714 8, and 0.710 9, respectively. In Tianjin, the relational degrees between the highway freight volume and average salary of on-job employees in urban units, GDP, tertiary industry output value, secondary industry output value, industrial added value, and per capita GDP are higher, and are 0.862 0, 0.855 6, 0.853 4, 0.851 4, 0.851 4, and 0.851 3, respectively. In conclusion, the analysis results of the relevant factors of the highway freight volumes in Beijing and Tianjin are generally consistent with those in Shaanxi Province, and the main relevant factors are the output values of the three major industries in these regions.

     

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