Analysis of the traffic conflict situation for speed probability distributions

Date

2023-03-30

Supervisor/s

Journal Title

Journal ISSN

Volume Title

Publisher

Cambridge University Press

Department

Type

Article

ISSN

0001-9240

Format

Citation

Oreg Zs, Shin H-S, Tsourdos A. (2023) Analysis of the traffic conflict situation for speed probability distributions. The Aeronautical Journal, Volume 127, Issue 1314, August 2023, pp. 1380-1434

Abstract

The increasingly widespread application of drones and the emergence of urban air mobility leads to a challenging question in airspace modernisation: how to create a safe and scalable air traffic management system that can handle the expected density of operations. Increasing the number of vehicles in a given airspace volume and enabling routine operations are essential for these services to be economically viable. However, a higher density of operations increases risks, poses a great challenge for coordination and necessitates the development of a novel solution for traffic management. This paper contributes to the research towards such a strategy and the field of airspace management by calculating and analysing the conflict probability in an en-route, free-flight scenario for autonomous vehicles. Analytical methods are used to determine the directional dependence of conflict probabilities for exponential and normal prescribed speed probability distributions. The notions of geometric and speed conflict are introduced and distinguished throughout the calculations of the paper. The effect of truncating the set of available flight speeds is also investigated. The sensitivity of the calculated results to speed and heading perturbations is studied within the analytical framework and verified by numerical simulations. Results enable a fresh approach to conflict detection and resolution through distribution shaping and are intended to be used in an integrated, stochastic coordination framework.

Description

Software Description

Software Language

Github

Keywords

autonomous systems, unmanned traffic management, analytical modelling, air traffic conflict detection, conflict detection systems, stochastic analysis

DOI

Rights

Attribution 4.0 International

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