Výsledky vyhledávání - "Mateos Villar, Manuel"

Machine learning for aircraft trajectory prediction: a solution for pre-tactical air traffic flow management

Autor: Mateos Villar, Manuel

Publikováno v: TDX (Tesis Doctorals en Xarxa)

Pla de Doctorats Industrials de la Generalitat de Catalunya (English) The goal of air traffic flow and capacity management (ATFCM) is to ensure that airport and airspace capacity meet traffic demand while optimising traffic flows to avoid exceeding t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______3484::b2399041834c2b3cfbe65955f4b987d7
https://hdl.handle.net/2117/38080810803/687428

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Unveiling airline preferences for pre-tactical route forecast through machine learning. An innovative system for ATFCM pre-tactical planning support

Autor: Mateos Villar, Manuel, Martín, Ignacio, Alcolea, Ruben, Herranz, Ricardo, García Cantú-Ros, Oliva, Prats Menéndez, Xavier

Publikováno v: UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)

In this work we describe a novel approach for the prediction of the flight plan to be sent by airspace users during the pre-tactical phase of Air Traffic Flow and Capacity Management (ATFCM). The proposed approach uses machine learning algorithms to

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::3f6053be82bee52d42e31ff225d157e9
http://hdl.handle.net/2117/359053

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Predicting requested flight levels with machine learning

Autor: Mateos Villar, Manuel, Martín, Ignacio, García, Pedro, Alcolea, Ruben, Herranz, Ricardo, Garcia, Oliva, Prats Menéndez, Xavier

Publikováno v: UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)

The objective of this paper is to present a machine learning approach for the prediction of the Requested Flight Level received during the pre-tactical phase of the Air Traffic Flow and Capacity Management process. A set of machine learning models ar

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::81bb00b99a27a05cd4459a22cb7eb646
http://hdl.handle.net/2117/341211

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Full-scale pre-tactical route prediction: machine learning to increase pre-tactical demand forecast accuracy

Autor: Mateos Villar, Manuel, Martín, Ignacio, García, Pedro, Herranz, Ricardo, Garcia, Oliva, Prats Menéndez, Xavier|||0000-0003-3717-4701

Publikováno v: UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)

The objective of this paper is to present an artificial intelligence-based methodology to predict the Flight plans that will be received during the pre-tactical phase of the Air Traffic Flow and Capacity Management (ATFCM) process. For this purpose,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::804de1de7055a0468c28114b9fed0f24
http://hdl.handle.net/2117/345147

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