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https://doi.org/10.21256/zhaw-28835| Publikationstyp: | Beitrag in wissenschaftlicher Zeitschrift |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Data-driven mid-air collision risk modelling using extreme-value theory |
| Autor/-in: | Figuet, Benoit Monstein, Raphael Waltert, Manuel Morio, Jérôme |
| et. al: | No |
| DOI: | 10.1016/j.ast.2023.108646 10.21256/zhaw-28835 |
| Erschienen in: | Aerospace Science and Technology |
| Band(Heft): | 142, Part A |
| Heft: | 108646 |
| Erscheinungsdatum: | 25-Sep-2023 |
| Verlag / Hrsg. Institution: | Elsevier |
| ISSN: | 1270-9638 1626-3219 |
| Sprache: | Englisch |
| Schlagwörter: | Aviation safety; Collision risk modelling; Extreme-value theory; Peaks over threshold; Monte Carlo simulation |
| Fachgebiet (DDC): | 380: Verkehr 510: Mathematik |
| Zusammenfassung: | Mid-air collision risk estimation is crucial for maintaining aviation safety and improving the efficiency of air traffic procedures. This paper introduces a novel, data-driven methodology for estimating the probability of mid-air collisions between aircraft by combining Monte Carlo simulation and the Peaks Over Threshold approach from Extreme Value Theory. This innovative approach has substantial advantages over traditional methods. Firstly, it reduces the number of assumptions about the traffic flow compared to traditional analytical methods. In fact, data-driven techniques require fewer assumptions, as they inherently capture the structures of the traffic flow within the underlying data. Secondly, it converges faster than methods based on crude Monte Carlo simulation. Notably, by employing Extreme Value Theory, this approach enables efficient evaluation of low-probabilities, which are commonly found in collision risk modelling. The effectiveness of the proposed methodology is demonstrated through estimating the probability of a mid-air collision in a real-world practical example. The case study investigates the risk of collisions between departures and go-arounds in the terminal airspace at Zurich Airport, highlighting the potential for improved safety and efficiency in air traffic management. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/28835 |
| 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_Figuet-etal_Data-driven-mid-air-collision-risk-modelling-extreme-value-theory.pdf | 2.55 MB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
Figuet, B., Monstein, R., Waltert, M., & Morio, J. (2023). Data-driven mid-air collision risk modelling using extreme-value theory. Aerospace Science and Technology, 142, Part A(108646). https://doi.org/10.1016/j.ast.2023.108646
Figuet, B. et al. (2023) ‘Data-driven mid-air collision risk modelling using extreme-value theory’, Aerospace Science and Technology, 142, Part A(108646). Available at: https://doi.org/10.1016/j.ast.2023.108646.
B. Figuet, R. Monstein, M. Waltert, and J. Morio, “Data-driven mid-air collision risk modelling using extreme-value theory,” Aerospace Science and Technology, vol. 142, Part A, no. 108646, Sep. 2023, doi: 10.1016/j.ast.2023.108646.
FIGUET, Benoit, Raphael MONSTEIN, Manuel WALTERT und Jérôme MORIO, 2023. Data-driven mid-air collision risk modelling using extreme-value theory. Aerospace Science and Technology. 25 September 2023. Bd. 142, Part A, Nr. 108646. DOI 10.1016/j.ast.2023.108646
Figuet, Benoit, Raphael Monstein, Manuel Waltert, and Jérôme Morio. 2023. “Data-Driven Mid-Air Collision Risk Modelling Using Extreme-Value Theory.” Aerospace Science and Technology 142, Part A (108646). https://doi.org/10.1016/j.ast.2023.108646.
Figuet, Benoit, et al. “Data-Driven Mid-Air Collision Risk Modelling Using Extreme-Value Theory.” Aerospace Science and Technology, vol. 142, Part A, no. 108646, Sept. 2023, https://doi.org/10.1016/j.ast.2023.108646.
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