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

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dc.contributor.author Jardines, Aniel
dc.contributor.author Soler, Manuel
dc.contributor.author García-Heras Carretero, Javier
dc.date.accessioned 2022-03-16T11:10:39Z
dc.date.available 2022-03-16T11:10:39Z
dc.date.issued 2021-08
dc.identifier.bibliographicCitation 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). Elsevier BV.
dc.identifier.issn 0969-6997
dc.identifier.uri http://hdl.handle.net/10016/34391
dc.description.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.
dc.description.sponsorship This work is partially supported through the Spanish Government initiative Proyectos de I+D+I "RETOS INVESTIGACIN"; Ministerio de Ciencia, Innovación y Universidades by the project entitled "Management of Meteorological Uncertainty for More Efficient Air Traffic: Meteorological Data Provision and Thunderstorm Avoidance" (RTI2018-098471-B-C32 ).
dc.format.extent 11
dc.language.iso eng
dc.publisher ELSEVIER BV
dc.rights © 2021 The Authors. Published by Elsevier Ltd.
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Air traffic flow management
dc.subject.other Atm data
dc.subject.other Data science
dc.subject.other Machine learning
dc.subject.other Thunderstorms
dc.title Estimating entry counts and ATFM regulations during adverse weather conditions using machine learning
dc.type article
dc.subject.eciencia Aeronáutica
dc.identifier.doi https://doi.org/10.1016/j.jairtraman.2021.102109
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. RTI2018-098471-B-C32
dc.type.version publishedVersion
dc.identifier.publicationfirstpage 102109
dc.identifier.publicationlastpage 102120
dc.identifier.publicationtitle JOURNAL OF AIR TRANSPORT MANAGEMENT
dc.identifier.publicationvolume 95
dc.identifier.uxxi AR/0000028926
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