Data-driven airborne collision risk modelling using a probability density function

Figuet, Benoit; Monstein, Raphael; Steven, Barry

doi:10.21256/zhaw-28806

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-28806

Publication type:	Conference paper
Type of review:	Peer review (publication)
Title:	Data-driven airborne collision risk modelling using a probability density function
Authors:	Figuet, Benoit Monstein, Raphael Steven, Barry
et. al:	No
DOI:	10.21256/zhaw-28806
Conference details:	15th Air Traffic Management Research and Development Seminar, Savannah, USA, 5-9 June 2023
Issue Date:	7-Jun-2023
Publisher / Ed. Institution:	ATM Seminar
Language:	English
Subjects:	Collision risk modelling; Data-driven; Probability density function; Kernel density estimation; Safety
Subject (DDC):	380: Transportation 510: Mathematics
Abstract:	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
Fulltext version:	Published version
License (according to publishing contract):	Licence according to publishing contract
Departement:	School of Engineering
Organisational Unit:	Centre for Aviation (ZAV)
Appears in collections:	Publikationen School of Engineering

Files in This Item:

File	Description	Size	Format
2023_Figuet-etal_Data-driven-airborne-collision-risk-modelling_ATM.pdf		1.13 MB	Adobe PDF	View/Open

Show full item record

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.