Abstract
The large amount of aircraft trajectory data publicly available through open data sources like the OpenSky Network presents a wide range of possibilities for monitoring and post-operational analysis of air traffic performance. This contribution addresses the automatic identification of operational events associated with trajectories. This is a challenging task that can be tackled with both empirical, rule-based methods and statistical, data-driven approaches. In this paper, we first propose a taxonomy of significant events, including usual operations such as take-off, Instrument Landing System (ILS) landing and holding, as well as less usual operations like firefighting, in-flight refuelling and navigational calibration. Then, we introduce different rule-based and statistical methods for detecting a selection of these events. The goal is to compare candidate methods and to determine which of the approaches performs better in each situation.
Original language | English |
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Number of pages | 10 |
Journal | Proceedings |
Volume | 59 |
Issue number | 4 |
DOIs | |
Publication status | Published - 2020 |
Event | 8th OpenSky Symposium 2020 - Virtual/online event due to COVID-19 Duration: 12 Nov 2020 → 13 Nov 2020 Conference number: 8 |
Keywords
- aircraft trajectories
- anomaly detection
- ADS-B
- pattern detection