IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v79y2009i6p1926-1934.html
   My bibliography  Save this article

An algorithm for the recognition of levels of congestion in road traffic problems

Author

Listed:
  • Lozano, Angélica
  • Manfredi, Giuseppe
  • Nieddu, Luciano

Abstract

Detection and recognition of the level of congestion at an intersection is a very important problem and a valuable source of information in traffic management. Although it is just one of all the aspects that make up a traffic management system, it seems to be a crucial point for gathering information. In this paper, we present a technique based on a k-means clustering algorithm for classification, which has been already successfully used in a number of pattern recognition problems, namely: as an algorithm for face recognition problems and in a number of medical diagnosis problems and it compares very well with the state of the art techniques.

Suggested Citation

  • Lozano, Angélica & Manfredi, Giuseppe & Nieddu, Luciano, 2009. "An algorithm for the recognition of levels of congestion in road traffic problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(6), pages 1926-1934.
  • Handle: RePEc:eee:matcom:v:79:y:2009:i:6:p:1926-1934
    DOI: 10.1016/j.matcom.2007.06.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475407002054
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.matcom.2007.06.008?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. Cheng, Zeyang & Wang, Wei & Lu, Jian & Xing, Xue, 2020. "Classifying the traffic state of urban expressways: A machine-learning approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 411-428.

    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:eee:matcom:v:79:y:2009:i:6:p:1926-1934. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    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.