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https://doi.org/10.21256/zhaw-28806| Publikationstyp: | Konferenz: Paper |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Data-driven airborne collision risk modelling using a probability density function |
| Autor/-in: | Figuet, Benoit Monstein, Raphael Steven, Barry |
| et. al: | No |
| DOI: | 10.21256/zhaw-28806 |
| Angaben zur Konferenz: | 15th Air Traffic Management Research and Development Seminar, Savannah, USA, 5-9 June 2023 |
| Erscheinungsdatum: | 7-Jun-2023 |
| Verlag / Hrsg. Institution: | ATM Seminar |
| Sprache: | Englisch |
| Schlagwörter: | Collision risk modelling; Data-driven; Probability density function; Kernel density estimation; Safety |
| Fachgebiet (DDC): | 380: Verkehr 510: Mathematik |
| Zusammenfassung: | This 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/28806 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| 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-airborne-collision-risk-modelling_ATM.pdf | 1.13 MB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
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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