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Volume 13
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Proceeding Paper

On the Use of Deep Neural Networks to Improve Flights Estimated Time of Arrival Predictions †

by
Jorge Silvestre
1,*,
Miguel de Santiago
1,
Anibal Bregon
1,
Miguel A. Martínez-Prieto
1 and
Pedro C. Álvarez-Esteban
2
1
Departamento de Informática, Universidad de Valladolid, 47002 Valladolid, Spain
2
Departamento de Estadística e Investigación Operativa, Universidad de Valladolid, 47002 Valladolid, Spain
*
Author to whom correspondence should be addressed.
†
Presented at the 9th OpenSky Symposium, Brussels, Belgium, 18–19 November 2021.
Eng. Proc. 2021, 13(1), 3; https://doi.org/10.3390/engproc2021013003
Published: 25 December 2021
(This article belongs to the Proceedings of The 9th OpenSky Symposium)
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Abstract

Predictable operations are the basis of efficient air traffic management. In this context, accurately estimating the arrival time to the destination airport is fundamental to make tactical decisions about an optimal schedule of landing and take-off operations. In this paper, we evaluate different deep learning models based on LSTM architectures for predicting estimated time of arrival of commercial flights, mainly using surveillance data from OpenSky Network. We observed that the number of previous states of the flight used to make the prediction have great influence on the accuracy of the estimation, independently of the architecture. The best model, with an input sequence length of 50, has reported a MAE of 3.33 min and a RMSE of 5.42 min on the test set, with MAE values of 5.67 and 2.13 min 90 and 15 min before the end of the flight, respectively.
Keywords: estimated time of arrival; ADS-B; LSTM; Recurrent Neural Networks; deep learning; air traffic management estimated time of arrival; ADS-B; LSTM; Recurrent Neural Networks; deep learning; air traffic management

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MDPI and ACS Style

Silvestre, J.; de Santiago, M.; Bregon, A.; Martínez-Prieto, M.A.; Álvarez-Esteban, P.C. On the Use of Deep Neural Networks to Improve Flights Estimated Time of Arrival Predictions. Eng. Proc. 2021, 13, 3. https://doi.org/10.3390/engproc2021013003

AMA Style

Silvestre J, de Santiago M, Bregon A, Martínez-Prieto MA, Álvarez-Esteban PC. On the Use of Deep Neural Networks to Improve Flights Estimated Time of Arrival Predictions. Engineering Proceedings. 2021; 13(1):3. https://doi.org/10.3390/engproc2021013003

Chicago/Turabian Style

Silvestre, Jorge, Miguel de Santiago, Anibal Bregon, Miguel A. Martínez-Prieto, and Pedro C. Álvarez-Esteban. 2021. "On the Use of Deep Neural Networks to Improve Flights Estimated Time of Arrival Predictions" Engineering Proceedings 13, no. 1: 3. https://doi.org/10.3390/engproc2021013003

APA Style

Silvestre, J., de Santiago, M., Bregon, A., Martínez-Prieto, M. A., & Álvarez-Esteban, P. C. (2021). On the Use of Deep Neural Networks to Improve Flights Estimated Time of Arrival Predictions. Engineering Proceedings, 13(1), 3. https://doi.org/10.3390/engproc2021013003

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