IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-540-27912-9_60.html
   My bibliography  Save this book chapter

Research of Traffic Flow Forecasting Based on Grids

In: Current Trends in High Performance Computing and Its Applications

Author

Listed:
  • Guozhen Tan

    (Dalian University of Technology, Department of Computer Science and Engineering)

  • Hao Liu

    (Dalian University of Technology, Department of Computer Science and Engineering)

  • Wenjiang Yuan

    (Dalian University of Technology, Department of Computer Science and Engineering)

  • Chengxu Li

    (Dalian University of Technology, Department of Computer Science and Engineering)

Abstract

Summary The complexity of traffic road network and the huge amount of traffic flow data results in the complexity of traffic flow forecasting. Gird computing technology integrates the grid resources and provides the traffic flow forecasting problem with resource sharing and coordination abilities. In this paper, according to the correlation theory, a traffic flow forecasting algorithm based on back-propagation(BP) neural network for single road section has been put forward, followed with a grid computing model to meet the high-performance requirement of the forecasting process. Making full use of the coordination ability between the multi-nodes of the grid computing, this method solves the precious problems in traffic flow forecasting, such as low efficiency, low real-time, etc.

Suggested Citation

  • Guozhen Tan & Hao Liu & Wenjiang Yuan & Chengxu Li, 2005. "Research of Traffic Flow Forecasting Based on Grids," Springer Books, in: Wu Zhang & Weiqin Tong & Zhangxin Chen & Roland Glowinski (ed.), Current Trends in High Performance Computing and Its Applications, pages 451-456, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-27912-9_60
    DOI: 10.1007/3-540-27912-1_60
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:978-3-540-27912-9_60. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.