IDEAS home Printed from https://ideas.repec.org/a/taf/transp/v45y2022i6p473-497.html
   My bibliography  Save this article

A study on securing model usefulness through geographical scalability testing of winter weather model developed with big traffic data

Author

Listed:
  • Hyuk-Jae Roh

Abstract

Few previous studies have conducted spatial transferability of the winter traffic models’ parameters between homogeneous and heterogeneous road segments during the winter season. This research pursues the purpose of using traffic data collected from five WIM sites in Alberta, Canada. Winter traffic models are developed for one weigh-in-motion site, and the other four sites, each representing different traffic characteristics, are used to verify the spatial transferability of the developed model. This research aggregated the traffic data into three vehicle types to develop winter traffic models by associating traffic data with climatic information. This research has demonstrated that the winter traffic models developed for the roads serving one specific travel population can be transferred with high accuracy to homogeneous and heterogeneous road segments.

Suggested Citation

  • Hyuk-Jae Roh, 2022. "A study on securing model usefulness through geographical scalability testing of winter weather model developed with big traffic data," Transportation Planning and Technology, Taylor & Francis Journals, vol. 45(6), pages 473-497, August.
  • Handle: RePEc:taf:transp:v:45:y:2022:i:6:p:473-497
    DOI: 10.1080/03081060.2022.2132947
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03081060.2022.2132947
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/03081060.2022.2132947?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:45:y:2022:i:6:p:473-497. 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.