• DocumentCode
    1733105
  • Title

    Combined Prediction Research of City Traffic Flow Based On Genetic Algorithm

  • Author

    Yuecong, Song ; Wei, Hu ; Guotang, Bi

  • Author_Institution
    Mianyang Normal Univ., Mianyang
  • fYear
    2007
  • Abstract
    Intelligent transportation system is the best measure to solve the urban traffic jam in the world, Forecasting urban traffic network is the premise for developing urban intelligent transportation system.In this paper ,some important forecasting models,including the theory and characteristic,are discussed, and the factors are discussed to influence the forecasting model. In the end ,combined prediction of city traffic flow based on genetic algorithm is given, using the characteristics of genetic algorithm´s colony search,the new algorithm combines all kinds of algorithms,optimizes the prediction way of thinking , fully discovers the advantages of different algorithms, and turns out to be practical and productive.
  • Keywords
    forecasting theory; genetic algorithms; road traffic; search problems; city traffic flow; colony search; combined prediction research; forecasting models; genetic algorithm; intelligent transportation system; urban traffic jam; urban traffic network; Cities and towns; Communication system traffic control; Demand forecasting; Genetic algorithms; Intelligent transportation systems; Neural networks; Predictive models; Technology forecasting; Telecommunication traffic; Traffic control; Forecast; Intelligent Transportation System; combined prediction; genetic algorithm; traffic flow;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electronic Measurement and Instruments, 2007. ICEMI '07. 8th International Conference on
  • Conference_Location
    Xi´an
  • Print_ISBN
    978-1-4244-1136-8
  • Electronic_ISBN
    978-1-4244-1136-8
  • Type

    conf

  • DOI
    10.1109/ICEMI.2007.4351054
  • Filename
    4351054