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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-26907
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dc.contributor.authorKrauth, Timothé-
dc.contributor.authorLafage, Adrien-
dc.contributor.authorMorio, Jérôme-
dc.contributor.authorOlive, Xavier-
dc.contributor.authorWaltert, Manuel-
dc.date.accessioned2023-02-11T10:07:35Z-
dc.date.available2023-02-11T10:07:35Z-
dc.date.issued2023-
dc.identifier.issn2666-8270de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/26907-
dc.description.abstractAirspace design is subject to a multitude of constraints, which are mainly driven by the concern to keep the risk of mid-air collision below a target level of safety. For that purpose, Monte Carlo simulation methods can be applied to estimate aircraft conflict probability but require the accurate generation of artificial trajectories. Generative models allow to generate an infinite number of trajectories for air traffic procedures where only few observations are available. The generated trajectories must not only resemble observed trajectories in terms of statistical distributions but they should stay flyable and consider uncertainty due to weather, air traffic control, aircraft performances, or human factors. This paper focuses on the generation problem, and its main contribution lies in the adaptation of the Variational Autoencoder structure to the problem of 4-dimensional aircraft trajectories modelling using Temporal Convolutional Networks and a prior distribution composed of a Variational Mixture of Posteriors (VampPrior). The proposed model has been trained on trajectories in the Terminal Manoeuvre Area of Zurich airport, which have a particularly high degree of variability as air traffic controllers often take actions that deviate aircraft from the nominal approach procedure. The model has demonstrated great abilities to take into account such amount of uncertainty. Regarding metrics that evaluate the estimation of the statistical distribution of the observed trajectories, and the flyability of the generated ones, the proposed method outperforms traditional statistical methods by being able to generate more complex and realistic trajectories.de_CH
dc.language.isoende_CH
dc.publisherElsevierde_CH
dc.relation.ispartofMachine Learning with Applicationsde_CH
dc.rightshttps://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectAir traffic managementde_CH
dc.subjectDeep generative modelde_CH
dc.subjectMultivariate time-series generationde_CH
dc.subjectVariational autoencoderde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc380: Verkehrde_CH
dc.titleDeep generative modelling of aircraft trajectories in terminal maneuvering areasde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitZentrum für Aviatik (ZAV)de_CH
dc.identifier.doi10.1016/j.mlwa.2022.100446de_CH
dc.identifier.doi10.21256/zhaw-26907-
zhaw.funding.euNode_CH
zhaw.issue100446de_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume11de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedAeronautical Communicationde_CH
zhaw.webfeedDatalabde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Krauth, T., Lafage, A., Morio, J., Olive, X., & Waltert, M. (2023). Deep generative modelling of aircraft trajectories in terminal maneuvering areas. Machine Learning with Applications, 11(100446). https://doi.org/10.1016/j.mlwa.2022.100446
Krauth, T. et al. (2023) ‘Deep generative modelling of aircraft trajectories in terminal maneuvering areas’, Machine Learning with Applications, 11(100446). Available at: https://doi.org/10.1016/j.mlwa.2022.100446.
T. Krauth, A. Lafage, J. Morio, X. Olive, and M. Waltert, “Deep generative modelling of aircraft trajectories in terminal maneuvering areas,” Machine Learning with Applications, vol. 11, no. 100446, 2023, doi: 10.1016/j.mlwa.2022.100446.
KRAUTH, Timothé, Adrien LAFAGE, Jérôme MORIO, Xavier OLIVE und Manuel WALTERT, 2023. Deep generative modelling of aircraft trajectories in terminal maneuvering areas. Machine Learning with Applications. 2023. Bd. 11, Nr. 100446. DOI 10.1016/j.mlwa.2022.100446
Krauth, Timothé, Adrien Lafage, Jérôme Morio, Xavier Olive, and Manuel Waltert. 2023. “Deep Generative Modelling of Aircraft Trajectories in Terminal Maneuvering Areas.” Machine Learning with Applications 11 (100446). https://doi.org/10.1016/j.mlwa.2022.100446.
Krauth, Timothé, et al. “Deep Generative Modelling of Aircraft Trajectories in Terminal Maneuvering Areas.” Machine Learning with Applications, vol. 11, no. 100446, 2023, https://doi.org/10.1016/j.mlwa.2022.100446.


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