Anomalies in the Sky: Experiments with traffic densities and airport runway use

12 pages•Published: December 23, 2019

Abstract

Anomalies in the airspace can provide an indicator of critical events and changes which go beyond aviation. Devising techniques, which can detect abnormal patterns can provide intelligence and information ranging from weather to political events. This work presents our latest findings in detecting such anomalies in air traffic patterns using ADS-B data provided by the OpenSky network [8]. After discussion of specific problems in anomaly detection in air traffic data, we show an experiment in a regional setting, evaluating air traffic densities with the Gini index, and a second experiment investigating the runway use at Zurich airport. In the latter case, strong available ground truth data allows to better understand and confirm findings of different learning approaches.

Keyphrases: air traffic data, anomaly detection, machine learning

In: Christina Pöpper and Martin Strohmeier (editors). Proceedings of the 7th OpenSky Workshop 2019, vol 67, pages 51-62.

Links:	https://easychair.org/publications/paper/cZL9
	https://doi.org/10.29007/3lks

BibTeX entry

@inproceedings{OpenSky19:Anomalies_Sky_Experiments_with,
  author    = {Axel Tanner and Martin Strohmeier},
  title     = {Anomalies in the Sky: Experiments with traffic densities and airport runway use},
  booktitle = {Proceedings of the 7th OpenSky Workshop 2019},
  editor    = {Christina Pöpper and Martin Strohmeier},
  series    = {EPiC Series in Computing},
  volume    = {67},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/cZL9},
  doi       = {10.29007/3lks},
  pages     = {51-62},
  year      = {2019}}

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