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
Link To Document