IDEAS home Printed from https://ideas.repec.org/a/eee/jaitra/v95y2021ics0969699721000922.html
   My bibliography  Save this article

Estimating entry counts and ATFM regulations during adverse weather conditions using machine learning

Author

Listed:
  • Jardines, Aniel
  • Soler, Manuel
  • García-Heras, Javier

Abstract

In recent years, convective weather has been the cause of significant delays in the European airspace. With climate experts anticipating the frequency and intensity of convective weather to increase in the future, it is necessary to find solutions that mitigate the impact of convective weather events on the airspace system. Analysis of historical air traffic and weather data will provide valuable insight on how to deal with disruptive convective events in the future. We propose a methodology for processing and integrating historic traffic and weather data to enable the use of machine learning algorithms to predict network performance during adverse weather. In this paper we develop regression and classification supervised learning algorithms to predict airspace performance characteristics such as entry count, number of flights impacted by weather regulations, and if a weather regulation is active. Examples using data from the Maastricht Upper Area Control Centre are presented with varying levels of predictive performance by the machine learning algorithms. Data sources include Demand Data Repository from EUROCONTROL and the Rapid Developing Thunderstorm product from EUMETSAT.

Suggested Citation

  • Jardines, Aniel & Soler, Manuel & García-Heras, Javier, 2021. "Estimating entry counts and ATFM regulations during adverse weather conditions using machine learning," Journal of Air Transport Management, Elsevier, vol. 95(C).
    • Handle: RePEc:eee:jaitra:v:95:y:2021:i:c:s0969699721000922
    DOI: 10.1016/j.jairtraman.2021.102109
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0969699721000922
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jairtraman.2021.102109?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.

    References listed on IDEAS

    as
    1. Jarry, Gabriel & Delahaye, Daniel & Nicol, Florence & Feron, Eric, 2020. "Aircraft atypical approach detection using functional principal component analysis," Journal of Air Transport Management, Elsevier, vol. 84(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wandelt, Sebastian & Sun, Xiaoqian & Zhang, Anming, 2023. "AI-driven assistants for education and research? A case study on ChatGPT for air transport management," Journal of Air Transport Management, Elsevier, vol. 113(C).
    2. Künnen, Jan-Rasmus & Strauss, Arne K. & Ivanov, Nikola & Jovanović, Radosav & Fichert, Frank, 2023. "Leveraging demand-capacity balancing to reduce air traffic emissions and improve overall network performance," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhou, Yu & Kou, Gang & Guo, Zhen-Zhu & Xiao, Hui, 2023. "Availability analysis of shared bikes using abnormal trip data," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    2. Archimbaud, Aurore & Boulfani, Fériel & Gendre, Xavier & Nordhausen, Klaus & Ruiz-Gazen, Anne & Virta, Joni, 2021. "ICS for multivariate functional anomaly detection with applications to predictive maintenance and quality control," TSE Working Papers 21-1182, Toulouse School of Economics (TSE), revised Mar 2022.
    3. Zhu, Xinting & Hong, Ning & He, Fang & Lin, Yu & Li, Lishuai & Fu, Xiaowen, 2023. "Predicting aircraft trajectory uncertainties for terminal airspace design evaluation," Journal of Air Transport Management, Elsevier, vol. 113(C).

    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:eee:jaitra:v:95:y:2021:i:c:s0969699721000922. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/journal-of-air-transport-management/ .

    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.