Predicting Runway Configuration in the Context of the Congonhas Airport Using Machine Learning

Bruno Monteiro Franzini; Tábada Domingues; Ivan Matias da Silva; Pollyanne Evangelista da Silva; André Elias Melo; Carlos H. Q. Forster; Jean Phelipe de Oliveira Lima; Marcos R. O. A. Máximo

doi:10.29327/xxi-sitraer-2024.904960

Predicting Runway Configuration in the Context of the Congonhas Airport Using Machine Learning

Bruno Monteiro Franzini
Tábada Domingues
Ivan Matias da Silva
Pollyanne Evangelista da Silva
André Elias Melo
Carlos H. Q. Forster
Jean Phelipe de Oliveira Lima
Marcos R. O. A. Máximo

Airspace Control Institute (ICEA), Research Division · Autonomous Computational Systems Lab (LAB-SCA), Computer Science Division, Aeronautics Institute · of Technology (ITA)

Baixar PDF DOI: 10.29327/xxi-sitraer-2024.904960 Também publicado na Even3

Evento: XXI SITRAER – Fortaleza, CE, Brasil, 2024
Volume: Anais do XXI Simpósio de Transporte Aéreo – SITRAER 2024 · ISBN 978-65-272-1008-5 · DOI 10.29327/xxi-sitraer-2024
Páginas: 340–348
Editora: Even3 Publicações
Editores: Francisco Heber Lacerda de Oliveira, Fabiano Rocha, Giovanna Miceli Ronzani Borille, Evandro José da Silva, Mauro Caetano

Abstract

The complexity and criticality of airspace control, coupled with the increasing demand for flights, highlight the need for advanced tools to optimize and secure operations. Additionally, many variables influence the air traffic scenario and, given the difficulty of analytically assessing the impact of each variable, a possible strategy is to use Machine Learning techniques to more accurately assign the weight of each factor, by processing historical data. In this context, considering that one of the variables that most influences air traffic is runway reconfiguration, this paper presents the development of a system to predict which runway will be in use at Congonhas Airport (SBSP) using a Gradient Boosting technique, called LightGBM. A classification model based on LightGBM was developed using data from the Meteorological Aerodrome Report (METAR), Terminal Aerodrome Forecast (TAF), and Weather Research and Forecasting (WRF). The model makes predictions about possible runway reconfigurations, deciding which side of the runway will be used in the coming hours. With this data, an accuracy of 98% in predictions was achieved and when testing the model on a period outside the training and test dataset, an accuracy of 88% was obtained. These results highlight the efficiency of the developed model compared to the currently used rule-based methods, which achieved approximately 81%, according to related works. This work contributes by providing a better quantification of the influence of meteorological variables on airspace operations, according to a feature importance analysis, which can support a more efficient and safer airspace activity planning.

Keywords: Air Traffic Management; LightGBM; Meteorological Data; Runway Configuration

Como citar

ABNT

FRANZINI, Bruno Monteiro; DOMINGUES, Tábada; SILVA, Ivan Matias da; SILVA, Pollyanne Evangelista da; MELO, André Elias; FORSTER, Carlos H. Q.; LIMA, Jean Phelipe de Oliveira; MÁXIMO, Marcos R. O. A. Predicting Runway Configuration in the Context of the Congonhas Airport Using Machine Learning. In: SIMPÓSIO DE TRANSPORTE AÉREO, 21., 2024, Fortaleza. Anais [...]. Recife: Even3, 2024. p. 340-348. ISBN 978-65-272-1008-5. DOI: 10.29327/xxi-sitraer-2024.904960. Disponível em: https://sitraer-sbta.org/anais-2024-904960.html.

APA

Franzini, B. M., Domingues, T., Silva, I. M., Silva, P. E., Melo, A. E., Forster, C. H. Q., Lima, J. P. O., & Máximo, M. R. O. A. (2024). Predicting Runway Configuration in the Context of the Congonhas Airport Using Machine Learning. In F. H. L. Oliveira, F. Rocha, G. M. R. Borille, E. J. Silva, & M. Caetano (Eds.), Anais do XXI Simpósio de Transporte Aéreo – SITRAER 2024 (pp. 340–348). Even3. https://doi.org/10.29327/xxi-sitraer-2024.904960

BibTeX

@inproceedings{franzini2024predicting,
  author = {Franzini, Bruno Monteiro and Domingues, Tábada and Silva, Ivan Matias da and Silva, Pollyanne Evangelista da and Melo, André Elias and Forster, Carlos H. Q. and Lima, Jean Phelipe de Oliveira and Máximo, Marcos R. O. A.},
  title = {{Predicting Runway Configuration in the Context of the Congonhas Airport Using Machine Learning}},
  booktitle = {Anais do XXI Simpósio de Transporte Aéreo – SITRAER 2024},
  editor = {Oliveira, Francisco Heber Lacerda de and Rocha, Fabiano and Borille, Giovanna Miceli Ronzani and Silva, Evandro José da and Caetano, Mauro},
  pages = {340--348},
  year = {2024},
  address = {Fortaleza, CE, Brasil},
  publisher = {Even3 Publicações},
  isbn = {978-65-272-1008-5},
  doi = {10.29327/xxi-sitraer-2024.904960},
  url = {https://sitraer-sbta.org/anais-2024-904960.html}
}