• DocumentCode
    2912946
  • Title

    Application of Grey-Markov model in predicting traffic volume

  • Author

    Qingfu, Li ; Qunfang, Hu ; Peng, Zhang

  • Author_Institution
    Zhengzhou Univ., Zhengzhou
  • fYear
    2007
  • fDate
    18-20 Nov. 2007
  • Firstpage
    707
  • Lastpage
    711
  • Abstract
    Generally, the planning of highway is designed on the basis of the traffic volume prediction. Because the influence factors of the traffic volume prediction are indeterminate, it leads to great discrepancy between the traffic prediction and the actual volume. Grey-markov forecasting model was founded by applying the model of GM (1,1) and Markov random process theory. The model utilizes the advantages of Grey-markov GM (1,1) forecasting model and Markov random process in order to discover the developing and varying tendency of the forecasting data sequences. When Markov random process is being used to decide the transfer rule of the state, it can not only make full use of the information of the historical data, but also improve the forecasting accuracy of random series. So it develops the applying range of grey forecasting and presents a new method of data sequences with large random. The analysis of an example indicates that the grey-markov model has good forecasting accuracy and excellent applicability.
  • Keywords
    Markov processes; grey systems; random processes; transportation; Markov random process theory; data sequence; grey-Markov forecasting model; highway planning; traffic volume prediction; Error analysis; Information analysis; Intelligent systems; Mathematical model; Predictive models; Random processes; Reliability engineering; Road accidents; Road transportation; Traffic control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Grey Systems and Intelligent Services, 2007. GSIS 2007. IEEE International Conference on
  • Conference_Location
    Nanjing
  • Print_ISBN
    978-1-4244-1294-5
  • Electronic_ISBN
    978-1-4244-1294-5
  • Type

    conf

  • DOI
    10.1109/GSIS.2007.4443366
  • Filename
    4443366