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.

    Highlights Machine learning algorithms can predict air traffic management behaviour. Spatial–temporal integration of historical air traffic and weather data. Weather data can predict traffic entry count and regulated entry count.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

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


    Beteiligte:


    Erscheinungsdatum :

    2021-06-25




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Using absorption areas to improve ATFM

    Ferchaud, F. / Vu Duong, / Gavoille, C. et al. | IEEE | 2004


    A new slot allocation for ATFM

    Duong, V. / Ferchaud, F. / Gavoille, C. et al. | IEEE | 2004


    Evaluating the Cost of ASM/ATFM Measures

    Kerlirzin, P. / Plusquellec, C. / IEEE et al. | British Library Conference Proceedings | 1997


    Pre-tactical prediction of ATFM delay for individual flights

    Mas-Pujol, Sergi / De Falco, Paolino / Salami, Esther et al. | IEEE | 2022


    Privacy-Preserving Implementation of an Auction Mechanism for ATFM Slot Swapping

    Feichtenschlager, Paul / Schuetz, Kevin / Jaburek, Samuel et al. | IEEE | 2023