• DocumentCode
    2632734
  • Title

    Applying Data Mining in Prediction and Classification of Urban Traffic

  • Author

    Nejad, Sedigheh Khajouei ; Seifi, Farid ; Ahmadi, Hamed ; Seifi, Nima

  • Author_Institution
    Dept. of Comput., Islamic Azad Univ., Sirjan, Iran
  • Volume
    3
  • fYear
    2009
  • fDate
    March 31 2009-April 2 2009
  • Firstpage
    674
  • Lastpage
    678
  • Abstract
    Data mining is a branch of computer science which recently has a great use for enterprises. Applying data mining methods, huge databases have been analyzed and processed. Data mining techniques are usually hired to mine knowledge and models from enormous data sets for prediction of new events. Furthermore these techniques are commonly used in fields which generate great amount of data that can not be processed by ordinary methods. During the last decade traffic management became a new field of science which produced unlimited data, and this amount of data needed new methods to be processed. It is clear that one of the most important fields in traffic management is Traffic prediction. As a result data mining methods were chosen to generate dependable patterns. In this paper we applied Classification methods to learn traffic behavior and prediction of new events.
  • Keywords
    data mining; traffic engineering computing; computer science; data mining; traffic management; urban traffic classification; urban traffic prediction; Computer science; Data analysis; Data engineering; Data mining; Knowledge engineering; Sampling methods; Telecommunication traffic; Testing; Traffic control; Wireless sensor networks; Classification; Data Mining; Decision Tree; Prediction; Urban Traffic;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science and Information Engineering, 2009 WRI World Congress on
  • Conference_Location
    Los Angeles, CA
  • Print_ISBN
    978-0-7695-3507-4
  • Type

    conf

  • DOI
    10.1109/CSIE.2009.906
  • Filename
    5170926