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

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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.
Air traffic flow management, Atm data, Data science, Machine learning, Thunderstorms
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Jardines, A., Soler, M., & García-Heras, J. (2021). Estimating entry counts and ATFM regulations during adverse weather conditions using machine learning. In Journal of Air Transport Management (Vol. 95, p. 102109).