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.
Positive and Unlabeled Learning for User Behavior Analysis Based on Mobile Internet Traffic Data
