Detecting Events in Aircraft Trajectories: Rule-Based and Data-Driven Approaches

Xavier Olive, J. Sun, Adrien Lafage, Luis Basora

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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 languageEnglish
Number of pages10
JournalProceedings
Volume59
Issue number4
DOIs
Publication statusPublished - 2020
Event8th OpenSky Symposium 2020 - Virtual/online event due to COVID-19
Duration: 12 Nov 202013 Nov 2020
Conference number: 8

Keywords

  • aircraft trajectories
  • anomaly detection
  • ADS-B
  • pattern detection

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