RT info:eu-repo/semantics/conferenceObject
T1 A data-driven approach for dynamic and adaptive aircraft trajectory prediction
A1 Rodríguez-Sanz, Álvaro
A1 Cordero García, José Manuel
A1 García Ovies-Carro, Icíar
A1 Iglesias, Enrique
K1 Aeropuertos
K1 Airports
K1 Ingeniería civil
K1 Civil engineering
K1 Transporte
K1 Transportation
AB Traffic Prediction (TP) is a key element in Air Traffic Management (ATM), as it plays afundamental role in adjusting capacity and available resources to current demand, as wellas in helping detect and solve potential conflicts. Moreover, the future implementation ofthe Trajectory Based Operations (TBO) concept will impose on aircraft the compliance ofvery accurately arrival times over designated points. In this sense, an improvement in TPaims at enabling an efficient management of the expected increase in air trafficstrategically, with tactical interventions only as a last resort. To achieve this objective, theATM system needs tools to support traffic and trajectory management functions, such asstrategic planning, trajectory negotiation and collaborative de-confliction. In all of thesetasks, trajectory and traffic prediction represents a cornerstone. The problem of achievingan accurate and reliable trajectory and traffic prediction has been tackled through differentmethodologies, with different levels of complexity. There are two main aspects to beconsidered when assessing the most appropriate forecasting methodology: (a) timehorizon:depending on the timescale (anticipation before the day of operations), the level ofuncertainty associated to the prediction will be different; and (b) input data: both the sourceand the quality of the input data (completeness, validity, accuracy, consistency, availabilityand timeliness) are key characteristics when assessing the viability of the prediction. Thisstudy develops a methodology for TP and traffic forecasting in a pre-tactical phase (oneday 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 thelevel of predictability of each of them. We propose a data-driven, dynamic and adaptive TPframework, which can be accommodated to different Airspace Users’ characteristics andstrategies.
PB Universidad de Burgos. Servicio de Publicaciones e Imagen Institucional
SN 978-84-18465-12-3
YR 2021
FD 2021-07
LK http://hdl.handle.net/10259/6849
UL http://hdl.handle.net/10259/6849
LA eng
NO 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
NO 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/).
DS Repositorio Institucional de la Universidad de Burgos
RD 19-abr-2024