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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-28806
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dc.contributor.authorFiguet, Benoit-
dc.contributor.authorMonstein, Raphael-
dc.contributor.authorSteven, Barry-
dc.date.accessioned2023-09-29T09:32:16Z-
dc.date.available2023-09-29T09:32:16Z-
dc.date.issued2023-06-07-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/28806-
dc.description.abstractThis paper introduces a novel data-driven mid-air collision risk model for an aircraft flying through a flow of aircraft, modelled using a probability density function to describe position, and a speed vector. The proposed model is, compared to traditional Monte-Carlo simulations, computationally efficient and, thus, facilitates exploration of risks as a function of key parameters, such as aircraft performance, or with different scenarios. Compared with traditional collision risk models, the proposed solution can handle more complex trajectories and traffic flows. The usefulness of the novel model is illustrated on a real-world example by applying it to the terminal airspace of Zurich airport, Switzerland. Specifically, the probability of collisions between go-arounds on Runway 14 and departures on Runway 16 is quantified. The results of the model were validated through comparison with Monte-Carlo simulations, with comparable outcomes but significantly lower computational costs.de_CH
dc.language.isoende_CH
dc.publisherATM Seminarde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectCollision risk modellingde_CH
dc.subjectData-drivende_CH
dc.subjectProbability density functionde_CH
dc.subjectKernel density estimationde_CH
dc.subjectSafetyde_CH
dc.subject.ddc380: Verkehrde_CH
dc.subject.ddc510: Mathematikde_CH
dc.titleData-driven airborne collision risk modelling using a probability density functionde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitZentrum für Aviatik (ZAV)de_CH
dc.identifier.doi10.21256/zhaw-28806-
zhaw.conference.details15th Air Traffic Management Research and Development Seminar, Savannah, USA, 5-9 June 2023de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Figuet, B., Monstein, R., & Steven, B. (2023, June 7). Data-driven airborne collision risk modelling using a probability density function. 15th Air Traffic Management Research and Development Seminar, Savannah, USA, 5-9 June 2023. https://doi.org/10.21256/zhaw-28806
Figuet, B., Monstein, R. and Steven, B. (2023) ‘Data-driven airborne collision risk modelling using a probability density function’, in 15th Air Traffic Management Research and Development Seminar, Savannah, USA, 5-9 June 2023. ATM Seminar. Available at: https://doi.org/10.21256/zhaw-28806.
B. Figuet, R. Monstein, and B. Steven, “Data-driven airborne collision risk modelling using a probability density function,” in 15th Air Traffic Management Research and Development Seminar, Savannah, USA, 5-9 June 2023, Jun. 2023. doi: 10.21256/zhaw-28806.
FIGUET, Benoit, Raphael MONSTEIN und Barry STEVEN, 2023. Data-driven airborne collision risk modelling using a probability density function. In: 15th Air Traffic Management Research and Development Seminar, Savannah, USA, 5-9 June 2023. Conference paper. ATM Seminar. 7 Juni 2023
Figuet, Benoit, Raphael Monstein, and Barry Steven. 2023. “Data-Driven Airborne Collision Risk Modelling Using a Probability Density Function.” Conference paper. In 15th Air Traffic Management Research and Development Seminar, Savannah, USA, 5-9 June 2023. ATM Seminar. https://doi.org/10.21256/zhaw-28806.
Figuet, Benoit, et al. “Data-Driven Airborne Collision Risk Modelling Using a Probability Density Function.” 15th Air Traffic Management Research and Development Seminar, Savannah, USA, 5-9 June 2023, ATM Seminar, 2023, https://doi.org/10.21256/zhaw-28806.


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