• DocumentCode
    2979855
  • Title

    Study on waterway freight volume forecast based on grey Markov

  • Author

    Lei, Fan ; Jun, Luo

  • Author_Institution
    Sch. of Transp., Wuhan Univ. of Technol., Wuhan, China
  • fYear
    2012
  • fDate
    22-24 June 2012
  • Firstpage
    119
  • Lastpage
    122
  • Abstract
    Waterway freight volume is not only an important indicator to determine the demand for waterway logistics, but also a major basis on which organization concerned determine the scale of the inland waterway infrastructure construction and make corresponding policies. The rationality and reliability of cargo forecast results will directly affect the investment income of logistics infrastructure and the development of related logistics enterprises. Meanwhile, it has great significant implications for the distribution of regional resources in a reasonable way and the formulation of logistics development strategy. After describing the grey GM (1,1) model and the basic principles of the Markov chain briefly, this article proposes a method of waterway freight volume forecast based on grey Marko model. The method combines the advantages of gray in the short-term forecasts and the Markov model to deal with advantage of the randomness and volatility data. At last, this paper take Hubei Province as an example for demonstration, results show that the grey Markov model is better than the grey.
  • Keywords
    Markov processes; forecasting theory; freight handling; goods distribution; investment; logistics; random processes; rivers; Grey Markov model; Hubei province; cargo forecast rationality; cargo forecast reliability; grey GM (1,1) model; inland waterway logistics infrastructure construction; investment income; logistics development strategy; logistics enterprises; randomness; regional resource distribution; volatility data; waterway freight volume forecasting; Markov processes; Markov; freight volume forecast; grey model;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Software Engineering and Service Science (ICSESS), 2012 IEEE 3rd International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4673-2007-8
  • Type

    conf

  • DOI
    10.1109/ICSESS.2012.6269419
  • Filename
    6269419