Anomaly Detection in Approach Operations at Sao Paulo/Guarulhos International Airport

Lucas Coelho e Silva; Mayara Condé Rocha Murça

Artigo premiado

Anomaly Detection in Approach Operations at Sao Paulo/Guarulhos International Airport

Lucas Coelho e Silva
Mayara Condé Rocha Murça

Instituto Tecnológico de Aeronáutica (ITA), Divisão de Engenharia Civil

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Evento: XIX SITRAER – São José dos Campos, SP, Brasil, 25 a 27 de outubro de 2022
Volume: Proceedings of SITRAER 2022 – Air Transportation Symposium · ISSN 2448-1912
Páginas: 174–183
Editora: Sociedade Brasileira de Pesquisa em Transporte Aéreo (SBTA)
Editores: Giovanna Miceli Ronzani Borille, Rogéria de Arantes Gomes, Evandro José da Silva, Mauro Caetano, Cláudia Azevedo Pereira

Abstract

The pursuit of stringent targets on operational safety and efficiency in an increasingly complex aviation system has been driving the development of novel analytics capabilities for more proactive aviation performance management. Anomaly detection in flight operations data is a prominent approach to delivering actionable information, as anomalies are often related to critical safety events or inefficient operations. In this paper, we apply machine learning techniques to aircraft surveillance data for offline anomaly detection and explanation in approach operations at Sao Paulo/Guarulhos International Airport (SBGR). First, we build an autoencoder classifier for the automatic identification of anomalous approach performance from flight trajectory data. Then, we extend our analysis with runway configuration and weather information to develop models for anomaly explanation. We found that the autoencoder classifier was able to detect operationally relevant anomalies, while the explanatory models provided novel insights about contributing factors to the anomalies identified. We learned that anomalous flight trajectories are more likely to be associated with landing operations on runway 27 under wind scenarios, with an increase in the odds ratio of 63% and 48% for tailwinds and headwinds, respectively. In addition, we also observed a positive association between anomalies and wind gusts situations.

Keywords: Air traffic management; Aviation safety; Anomaly detection; Trajectory data ana- lytics; Machine learning

Como citar

ABNT

SILVA, Lucas Coelho e; MURÇA, Mayara Condé Rocha. Anomaly Detection in Approach Operations at Sao Paulo/Guarulhos International Airport. In: SIMPÓSIO DE TRANSPORTE AÉREO, 19., 2022, São José dos Campos. Anais [...]. São José dos Campos: SBTA, 2022. p. 174-183. ISSN 2448-1912. Disponível em: https://sitraer-sbta.org/anais-2022-sit231.html.

APA

Silva, L. C., & Murça, M. C. R. (2022). Anomaly Detection in Approach Operations at Sao Paulo/Guarulhos International Airport. In G. M. R. Borille, R. A. Gomes, E. J. Silva, M. Caetano, & C. A. Pereira (Eds.), Proceedings of SITRAER 2022 – Air Transportation Symposium (pp. 174–183). SBTA. https://sitraer-sbta.org/anais-2022-sit231.html

BibTeX

@inproceedings{silva2022anomaly,
  author = {Silva, Lucas Coelho e and Murça, Mayara Condé Rocha},
  title = {{Anomaly Detection in Approach Operations at Sao Paulo/Guarulhos International Airport}},
  booktitle = {Proceedings of SITRAER 2022 – Air Transportation Symposium},
  editor = {Borille, Giovanna Miceli Ronzani and Gomes, Rogéria de Arantes and Silva, Evandro José da and Caetano, Mauro and Pereira, Cláudia Azevedo},
  pages = {174--183},
  year = {2022},
  address = {São José dos Campos, SP, Brasil},
  publisher = {Sociedade Brasileira de Pesquisa em Transporte Aéreo (SBTA)},
  issn = {2448-1912},
  url = {https://sitraer-sbta.org/anais-2022-sit231.html}
}