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

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dc.affiliation.dptoUC3M. Departamento de Ingeniería Aeroespaciales
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Ingeniería Aeroespaciales
dc.contributor.authorJardines, Aniel
dc.contributor.authorSoler, Manuel
dc.contributor.authorGarcía-Heras Carretero, Javier
dc.date.accessioned2022-03-16T11:10:39Z
dc.date.available2022-03-16T11:10:39Z
dc.date.issued2021-08
dc.description.abstractIn 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.en
dc.description.sponsorshipThis 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 ).en
dc.format.extent11
dc.identifier.bibliographicCitationJardines, 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).en
dc.identifier.doihttps://doi.org/10.1016/j.jairtraman.2021.102109
dc.identifier.issn0969-6997
dc.identifier.publicationfirstpage102109
dc.identifier.publicationlastpage102120
dc.identifier.publicationtitleJOURNAL OF AIR TRANSPORT MANAGEMENTen
dc.identifier.publicationvolume95
dc.identifier.urihttps://hdl.handle.net/10016/34391
dc.identifier.uxxiAR/0000028926
dc.language.isoengen
dc.publisherElsevieren
dc.relation.projectIDGobierno de España. RTI2018-098471-B-C32es
dc.relation.projectIDAT-2021
dc.rights© 2021 The Authors. Published by Elsevier Ltd.en
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaAeronáuticaes
dc.subject.otherAir traffic flow managementen
dc.subject.otherAtm dataen
dc.subject.otherData scienceen
dc.subject.otherMachine learningen
dc.subject.otherThunderstormsen
dc.titleEstimating entry counts and ATFM regulations during adverse weather conditions using machine learningen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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