IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v10y2019i1d10.1007_s13198-019-00775-z.html
   My bibliography  Save this article

The strategy of traffic congestion management based on case-based reasoning

Author

Listed:
  • Hao Zhang

    (AnHui University of Science and Technology
    Tongling University)

  • GuangLong Dai

    (AnHui University of Science and Technology)

Abstract

This paper proposes a case-based reasoning (CBR) method for traffic congestion management in view of the rapid development of urban motorization and the increasingly prominent problem of traffic congestion. The reasoning model based on CBR congestion management is established, and the characteristic attributes of traffic congestion cases are analyzed. The calculation methods combining local and global similarity are adopted for different types of attributes. Meanwhile, it proposes the update and preservation mode for traffic congestion case database. The cases indicate that traffic congestion management can quickly find a solution to traffic congestion problem by calculating the similarity between congestion cases through CBR. The cases prove that this method can improve the accuracy of CBR results and have certain guiding significance for traffic management.

Suggested Citation

  • Hao Zhang & GuangLong Dai, 2019. "The strategy of traffic congestion management based on case-based reasoning," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(1), pages 142-147, February.
    • Handle: RePEc:spr:ijsaem:v:10:y:2019:i:1:d:10.1007_s13198-019-00775-z
    DOI: 10.1007/s13198-019-00775-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-019-00775-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-019-00775-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tianhe Lan & Xiaojing Zhang & Dayi Qu & Yufeng Yang & Yicheng Chen, 2023. "Short-Term Traffic Flow Prediction Based on the Optimization Study of Initial Weights of the Attention Mechanism," Sustainability, MDPI, vol. 15(2), pages 1-16, January.

    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:ijsaem:v:10:y:2019:i:1:d:10.1007_s13198-019-00775-z. 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.