IDEAS home Printed from https://ideas.repec.org/a/taf/transp/v31y2007i5p525-544.html
   My bibliography  Save this article

Imputation of Missing Traffic Data during Holiday Periods

Author

Listed:
  • Zhaobin Liu
  • Satish Sharma
  • Sandeep Datla

Abstract

Highway and transportation agencies implement large-scale traffic monitoring programs to fulfill the planning, operation and management needs of highway systems. These monitoring programs typically use inductive loops as detectors to collect traffic data. Because of the harsh environment in which they operate, they are highly prone to malfunctioning and providing erroneous or missing data. If this occurs during holiday periods when the increase in highway traffic is often substantial, there is a good chance that traffic peaking and variation will be underestimated. This paper discusses the adaptability of available imputation techniques for holiday traffic and then introduces a new procedure using non-parametric regression -- the k-nearest neighbor (k-NN) method. It is found that the performance of the k-NN method is consistent and reasonable for different holidays and types of highway. In addition, it is also concluded that the data requirements for this method are flexible.

Suggested Citation

  • Zhaobin Liu & Satish Sharma & Sandeep Datla, 2007. "Imputation of Missing Traffic Data during Holiday Periods," Transportation Planning and Technology, Taylor & Francis Journals, vol. 31(5), pages 525-544, February.
    • Handle: RePEc:taf:transp:v:31:y:2007:i:5:p:525-544
    DOI: 10.1080/03081060802364505
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03081060802364505
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03081060802364505?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.

    More about this item

    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:taf:transp:v:31:y:2007:i:5:p:525-544. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/GTPT20 .

    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.