2026-04-29T08:25:44Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/68492024-05-17T10:18:44Zcom_10259.4_104com_10259_2604col_10259_6848
Rodríguez-Sanz, Álvaro
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600
Cordero García, José Manuel
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600
García Ovies-Carro, Icíar
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600
Iglesias, Enrique
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2022-09-14T12:17:40Z
2022-09-14T12:17:40Z
2021-07
978-84-18465-12-3
http://hdl.handle.net/10259/6849
10.36443/10259/6849
Trabajo presentado en: R-Evolucionando el transporte, XIV Congreso de Ingeniería del Transporte (CIT 2021), realizado en modalidad online los días 6, 7 y 8 de julio de 2021, organizado por la Universidad de Burgos
Traffic Prediction (TP) is a key element in Air Traffic Management (ATM), as it plays a
fundamental role in adjusting capacity and available resources to current demand, as well
as in helping detect and solve potential conflicts. Moreover, the future implementation of
the Trajectory Based Operations (TBO) concept will impose on aircraft the compliance of
very accurately arrival times over designated points. In this sense, an improvement in TP
aims at enabling an efficient management of the expected increase in air traffic
strategically, with tactical interventions only as a last resort. To achieve this objective, the
ATM system needs tools to support traffic and trajectory management functions, such as
strategic planning, trajectory negotiation and collaborative de-confliction. In all of these
tasks, trajectory and traffic prediction represents a cornerstone. The problem of achieving
an accurate and reliable trajectory and traffic prediction has been tackled through different
methodologies, with different levels of complexity. There are two main aspects to be
considered when assessing the most appropriate forecasting methodology: (a) timehorizon:
depending on the timescale (anticipation before the day of operations), the level of
uncertainty associated to the prediction will be different; and (b) input data: both the source
and the quality of the input data (completeness, validity, accuracy, consistency, availability
and timeliness) are key characteristics when assessing the viability of the prediction. This
study develops a methodology for TP and traffic forecasting in a pre-tactical phase (one
day to six days before the day of operations), when few or no flight plans are available.
This should be adjusted to different time scales (planning horizons), taking into account the
level of predictability of each of them. We propose a data-driven, dynamic and adaptive TP
framework, which can be accommodated to different Airspace Users’ characteristics and
strategies.
This study is part of the DIAPasON (A Data-drIven approach for dynamic and Adaptive trajectory PredictiON) project, funded by the ENGAGE KTN network (the SESAR Knowledge Transfer Network; https://engagektn.com/).
application/pdf
eng
Universidad de Burgos. Servicio de Publicaciones e Imagen Institucional
R-Evolucionando el transporte
https://doi.org/10.36443/9788418465123
Aeropuertos
Airports
Ingeniería civil
Transportes
Civil engineering
Transportation
A data-driven approach for dynamic and adaptive aircraft trajectory prediction
info:eu-repo/semantics/conferenceObject
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info:eu-repo/semantics/openAccess
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10259/6849
oai:riubu.ubu.es:10259/6849
2024-05-17 12:18:44.863
Repositorio Institucional de la Universidad de Burgos
bubrep@ubu.es
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