Working Paper Article Version 1 This version is not peer-reviewed

Detection and Identification of Significant Events in Historical Aircraft Trajectory Data

Version 1 : Received: 20 December 2019 / Approved: 21 December 2019 / Online: 21 December 2019 (12:23:31 CET)

How to cite: Olive, X.; Basora, L. Detection and Identification of Significant Events in Historical Aircraft Trajectory Data. Preprints 2019, 2019120285 Olive, X.; Basora, L. Detection and Identification of Significant Events in Historical Aircraft Trajectory Data. Preprints 2019, 2019120285

Abstract

A large amount of data is produced every day by stakeholders of the Air Traffic Management (ATM) system, in particular airline operators, airports, and air navigation service provider (ANSP). Most data is kept private for many reasons, including commercial and security concerns. More than data, shared information is precious, as it leverages intelligent decision-making support tools designed to smooth daily operations. We present a framework to detect, identify and characterise anomalies in past aircraft trajectory data. It is based on an open source of ADS-B based aircraft trajectories, and extracted information can benefit a wide range of stakeholders: Air Traffic Control (ATC) training centres could play more realistic simulations; ANSP may improve capacity indicators; academics improve safety models and risk estimations; and commercial stakeholders, like airlines and airports, may use such information to improve short-term predictions and optimise their operations. The technique is based on autoencoding artificial neural networks applied on flows of trajectories, which provide a useful reading grid associating cluster analysis with quantified level of abnormality. In particular, we find that the highest anomaly scores correspond to poor weather conditions, whereas anomalies with a lower score relate to ATC tactical actions.

Subject Areas

trajectory data analytics; air traffic flows; anomaly detection; air traffic management; machine learning; autoencoders

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