IDEAS home Printed from https://ideas.repec.org/a/taf/transp/v42y2019i6p554-572.html
   My bibliography  Save this article

Forecasting road traffic conditions using a context-based random forest algorithm

Author

Listed:
  • Jonny Evans
  • Ben Waterson
  • Andrew Hamilton

Abstract

With the ability to accurately forecast road traffic conditions several hours, days and even months ahead of time, both travellers and network managers can take pro-active measures to minimise congestion, saving time, money and emissions. This study evaluates a previously developed random forest algorithm, RoadCast, which was designed to achieve this task. RoadCast incorporates contexts using machine learning to forecast more accurately contexts such as public holidays, sporting events and school term dates. This paper evaluates the potential of RoadCast as a traffic forecasting algorithm for use in Intelligent Transport System applications. Tests are undertaken using a number of different forecast horizons and varying amounts of training data, and an implementation procedure is recommended.

Suggested Citation

  • Jonny Evans & Ben Waterson & Andrew Hamilton, 2019. "Forecasting road traffic conditions using a context-based random forest algorithm," Transportation Planning and Technology, Taylor & Francis Journals, vol. 42(6), pages 554-572, August.
    • Handle: RePEc:taf:transp:v:42:y:2019:i:6:p:554-572
    DOI: 10.1080/03081060.2019.1622250
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03081060.2019.1622250
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03081060.2019.1622250?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. Bita Etaati & Arash Jahangiri & Gabriela Fernandez & Ming-Hsiang Tsou & Sahar Ghanipoor Machiani, 2023. "Understanding Active Transportation to School Behavior in Socioeconomically Disadvantaged Communities: A Machine Learning and SHAP Analysis Approach," Sustainability, MDPI, vol. 16(1), pages 1-18, December.
    2. Yali Zhang & Luchao Bai & Yuan Qi & Huasheng Huang & Xiaoyang Lu & Junqi Xiao & Yubin Lan & Muhua Lin & Jizhong Deng, 2022. "Detection of Rice Spikelet Flowering for Hybrid Rice Seed Production Using Hyperspectral Technique and Machine Learning," Agriculture, MDPI, vol. 12(6), pages 1-17, May.

    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:42:y:2019:i:6:p:554-572. 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.