Naslov
Machine Learning classification techniques applied to static air traffic conflict detection
(Machine Learning classification techniques applied to static air traffic conflict detection)

Autori
Pérez-Castán, Javier Alberto ; Pérez-Sanz, Luis ; Bowen-Varela, Jaime ; Serrano-Mira, Lidia ; Radišić, Tomislav ; Feuerle, Thomas

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
IOP Conference Series: Materials Science and Engineering / Guadagno, Liberata ; Strohmayer, Andreas ; Pantelakis, Spiros - Barcelona : IOP Publishing, 2022, 1-8

Skup
11th EASN International Conference on Innovation in Aviation and Space to the Satisfaction of the European Citizens

Mjesto i datum
Barcelona, Španjolska, 01.09.2021. - 03.09.2021

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Conflict detection ; Machine learning

Sažetak
This article evaluates Machine Learning (ML) classification techniques applied to air-traffic conflict detection. The methodology develops a static approach in which the conflict prediction is performed when an aircraft pierces into the airspace. Conflict detection does not evaluate separation infringements but a Situation of Interest (SI). An aircraft pair constitutes a SI when it is expected to get with a horizontal separation between both aircraft closer than 10 Nautical Miles (NM) and a vertical separation closer than 1000 feet (ft). Therefore, the ML predictor classifies aircraft pairs between SI or No SI pairs. Air traffic information is extracted from The OpenSky Network that provides ADS-B trajectories. ADS-B trajectories do not offer enough SI samples to be evaluated. Hence, the authors performed simulations varying the entry time of the trajectories to the airspace within the same time period. The methodology was applied to a portion of Switzerland airspace, and simulations reached a 5% rate of SI samples. Cost-sensitive techniques were used to handle the strong imbalance of the database. Two experiments were performed: the Pure model considered the whole database, and the Hybrid model considered aircraft pairs that intersect horizontally closer than 20 NM and vertically lower than 1000 ft. The Hybrid model provided the best results achieving 72% recall, representing the success rate of Missed alerts and 82% accuracy, which means the whole predictions' success rate.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Tehnologija prometa i transport, Zrakoplovstvo, raketna i svemirska tehnika

POVEZANOST RADA