IDEAS home Printed from https://ideas.repec.org/a/taf/tjrtxx/v10y2022i1p24-43.html
   My bibliography  Save this article

Prediction of track geometry degradation using artificial neural network: a case study

Author

Listed:
  • Hamid Khajehei
  • Alireza Ahmadi
  • Iman Soleimanmeigouni
  • Mohammad Haddadzade
  • Arne Nissen
  • Mohammad Javad Latifi Jebelli

Abstract

The aim of this study has been to predict the track geometry degradation rate using artificial neural network. Tack geometry measurements, asset information, and maintenance history for five line sections from the Swedish railway network were collected, processed, and prepared to develop the ANN model. The information of track was taken into account and different features of track sections were considered as model input variables. In addition, Garson method was applied to explore the relative importance of the variables affecting geometry degradation rate. By analysing the performance of the model, we found out that the ANN has an acceptable capability in explaining the variability of degradation rates in different locations of the track. In addition, it is found that the maintenance history, the degradation level after tamping, and the frequency of trains passing along the track have the strongest contributions among the considered set of features in prediction of degradation rate.

Suggested Citation

  • Hamid Khajehei & Alireza Ahmadi & Iman Soleimanmeigouni & Mohammad Haddadzade & Arne Nissen & Mohammad Javad Latifi Jebelli, 2022. "Prediction of track geometry degradation using artificial neural network: a case study," International Journal of Rail Transportation, Taylor & Francis Journals, vol. 10(1), pages 24-43, January.
  • Handle: RePEc:taf:tjrtxx:v:10:y:2022:i:1:p:24-43
    DOI: 10.1080/23248378.2021.1875065
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/23248378.2021.1875065
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/23248378.2021.1875065?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:tjrtxx:v:10:y:2022:i:1:p:24-43. 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/tjrt20 .

    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.