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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-28835
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dc.contributor.authorFiguet, Benoit-
dc.contributor.authorMonstein, Raphael-
dc.contributor.authorWaltert, Manuel-
dc.contributor.authorMorio, Jérôme-
dc.date.accessioned2023-10-05T14:41:05Z-
dc.date.available2023-10-05T14:41:05Z-
dc.date.issued2023-09-25-
dc.identifier.issn1270-9638de_CH
dc.identifier.issn1626-3219de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/28835-
dc.description.abstractMid-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.de_CH
dc.language.isoende_CH
dc.publisherElsevierde_CH
dc.relation.ispartofAerospace Science and Technologyde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectAviation safetyde_CH
dc.subjectCollision risk modellingde_CH
dc.subjectExtreme-value theoryde_CH
dc.subjectPeaks over thresholdde_CH
dc.subjectMonte Carlo simulationde_CH
dc.subject.ddc380: Verkehrde_CH
dc.subject.ddc510: Mathematikde_CH
dc.titleData-driven mid-air collision risk modelling using extreme-value theoryde_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.ast.2023.108646de_CH
dc.identifier.doi10.21256/zhaw-28835-
zhaw.funding.euNode_CH
zhaw.issue108646de_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume142, Part Ade_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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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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