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Modeling and Detecting Anomalous Safety Events in Approach Flights Using ADS-B Data

Paper ID

ATM-2021-069

Conference

USA/Europe ATM R&D Seminar

Year

2021

Theme

Safety, resilience and security

Project Name

Keywords:

ADS-B, anomaly detection, data mining, flight safety, safety monitoring, Schiphol Airport

Authors

Alberto Bonifazi, Junzi Sun, Gerben van Baren and Jacco Hoekstra

DOI

Project Number

Abstract

Not all flight data anomalies correspond to operational safety concerns. But anomalous safety events can be linked to anomalies in flight data. During the final phases of a flight, two significant safety events are unstable approach and go-around. In this paper, using Automatic Dependent Surveillance-Broadcast (ADS-B) data, we develop several exceedance and anomaly detection techniques to identify these events. Rule-based algorithms and data-driven Gaussian Mixture Models (GMM) are proposed to identify unstable approaches. A fuzzy logic approach is developed to model and to identify go-arounds. We extend our analysis combining runway information and meteorological reports to provide deeper insights on flight safety during the approach. These identification models are also applied to the ADS-B data from the Schiphol Airport area in Amsterdam in 2018. By using a reference report provided by the Dutch transportation regulatory agency, the chosen GMM model can identify 25% to 30% of reported unstable approaches, and the go-around detection model can identify 98% of go-arounds.