Analysis of Influencing Factors of Integrated Freight Transport Volume Based on Gray Markov Model

  • Ya-ping Zhang
  • Yue-e Gao
  • Yan-wen XieEmail author
  • Shou-ming Qi
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 503)


To get the quantitative analysis of the influencing factors of the freight traffic volume, the Gray correlation method is used to analyze and filter 3 factors, which have significant influence between the volume of total freight and volume of freight transport (5 kinds of transportation modes) with 14 influencing factors. And the forecasting accuracy of the Gray Markov forecasting model is used to screen the main influence factor on the volume of freight transport, which is based on the Gray relational analysis results. The calculation results show that the impact on volume of freight transport is mainly due to the economic factors, followed by the size of the transport, transport destination, and transportation modes between the internal competition and other types of factors.


Integrated freight transport Gray relational analysis Gray Markov model Influencing factors 


  1. 1.
    Zhou J, Fu Z, Tian M (2015) Construction of China’s transportation service index based on CTSI index. J Syst Eng Pract 35(4):965–972Google Scholar
  2. 2.
    Rang MS, Kang MG, Park SW et al (2006) Application of grey model and artificial neural networks to flood forecasting 1. Jawra J Am Water Resour Assoc 42(2):473–486CrossRefGoogle Scholar
  3. 3.
    Ke Z (2013) Random simulation method for accuracy test of grey prediction model. Grey Syst 3(1):26–34CrossRefGoogle Scholar
  4. 4.
    Ma J, Chen Z. Tse K (2013) Forecast of civil aviation volume of freight transport using unbiased grey-fuzzy-Markov chain method. In: International conference on information management, innovation management and industrial engineering, vol 3. pp 528–531Google Scholar
  5. 5.
    Song R (2015) Application of fuzzy linear regression model in predicting the volume of freight transport of city. J Inf Comput Sci 12(1):191–200CrossRefGoogle Scholar
  6. 6.
    Gu S, Lu X (2015) Analysis of China railway passenger volume’s influence factors based on principal component regression. In: International conference on logistics, informatics and service sciences, pp 1–5Google Scholar
  7. 7.
    Guo K, Ma Y, Wang T (2009) Prediction of volume of railway freight based on improved gray-markov chain method. J Lanzhou Jiao tong Univ 28(6):124–127Google Scholar
  8. 8.
    Zhang W, Cui S, Deng H (2011) Mark Markov forecasting model for highway freight transport volume. J Wuhan Univ Technol Transp Sci Eng 35(4):658–661Google Scholar
  9. 9.
    Wang Q, Wang X, Xia F (2009) Integration of grey model and multiple regression model to predict energy consumption. In: International conference on energy and environment technology. IEEE Computer Society, pp 194–197Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Ya-ping Zhang
    • 1
  • Yue-e Gao
    • 1
    • 2
  • Yan-wen Xie
    • 1
    Email author
  • Shou-ming Qi
    • 1
  1. 1.Transportation Planning and Management, School of Transportation Science and EngineeringHarbin Institute of TechnologyHarbinChina
  2. 2.China Transportation AssociationBeijingChina

Personalised recommendations