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https://doi.org/10.21256/zhaw-26907| Publikationstyp: | Beitrag in wissenschaftlicher Zeitschrift |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Deep generative modelling of aircraft trajectories in terminal maneuvering areas |
| Autor/-in: | Krauth, Timothé Lafage, Adrien Morio, Jérôme Olive, Xavier Waltert, Manuel |
| et. al: | No |
| DOI: | 10.1016/j.mlwa.2022.100446 10.21256/zhaw-26907 |
| Erschienen in: | Machine Learning with Applications |
| Band(Heft): | 11 |
| Heft: | 100446 |
| Erscheinungsdatum: | 2023 |
| Verlag / Hrsg. Institution: | Elsevier |
| ISSN: | 2666-8270 |
| Sprache: | Englisch |
| Schlagwörter: | Air traffic management; Deep generative model; Multivariate time-series generation; Variational autoencoder |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren 380: Verkehr |
| Zusammenfassung: | Airspace 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/26907 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | CC BY 4.0: Namensnennung 4.0 International |
| Departement: | School of Engineering |
| Organisationseinheit: | Zentrum für Aviatik (ZAV) |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2023_Krauth-etal_Deep-generative-modelling-of-aircraft-trajectories.pdf | 3.46 MB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
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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