IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v54y2016i12p3465-3474.html
   My bibliography  Save this article

Improve urban passenger transport management by rationally forecasting traffic congestion probability

Author

Listed:
  • Xuesong Feng
  • Mitsuru Saito
  • Yi Liu

Abstract

A Bayesian network (BN) approach is proposed in this study to analyse the overall traffic congestion probability of an urban road network in consideration of the influence of applying various transport policies. The continually expanding urbanised region of Beijing has been chosen as the study area because of its rapid expansion and motorisation, which lead to the severe traffic congestion occurring nearly every day. It is demonstrated that the proposed BN approach is able to rationally predict the probability of the overall traffic congestion that will take place given a certain transport policy. It is also proven that increasing the number of buses providing convenient passenger transport service in the urbanised region of Beijing will most effectively reduce the probability of the traffic congestion in this area, especially when the newly constructed roads in the same region are put into use.

Suggested Citation

  • Xuesong Feng & Mitsuru Saito & Yi Liu, 2016. "Improve urban passenger transport management by rationally forecasting traffic congestion probability," International Journal of Production Research, Taylor & Francis Journals, vol. 54(12), pages 3465-3474, June.
    • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:12:p:3465-3474
    DOI: 10.1080/00207543.2015.1062570
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2015.1062570
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207543.2015.1062570?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. Navin Ranjan & Sovit Bhandari & Pervez Khan & Youn-Sik Hong & Hoon Kim, 2021. "Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder," Sustainability, MDPI, vol. 13(9), pages 1-26, May.
    2. Tanzina Afrin & Nita Yodo, 2020. "A Survey of Road Traffic Congestion Measures towards a Sustainable and Resilient Transportation System," Sustainability, MDPI, vol. 12(11), pages 1-23, June.

    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:tprsxx:v:54:y:2016:i:12:p:3465-3474. 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/TPRS20 .

    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.