Chollet, F., et al.: Keras (2015). https://keras.io

[Otros] New paradigms in aviation, as the expected shortage of qualified pilots and the increasing number of flights worldwide, present big challenges to aeronautic enterprises and regulators. In this sense, a concept known as Single Pilot Operations arises in the task of dealing with these challenges, for which, automation becomes necessary, especially in Air Traffic Management. In this regard, this paper presents a deep learning-based approach to leveraging the job of both ground controllers and pilots. Making use of Meteorological Terminal Air Reports, obtained regularly from every aerodrome worldwide, we created a model based on a multi-layer perceptron capable of determining the approach trajectory of an aircraft thirty minutes prior to the expected landing time. Experiments on aircraft trajectories from Toulouse to Seville, show an accuracy, recall and F1-score higher than 0.9 for the resultant predictive model. This work has received funding from the Clean Sky 2 Joint Undertaking (JU) under grant agreement No 831884. The Titan V used for this research was donated by the NVIDIA Corporation Jiménez-Campfens, N.; Colomer, A.; Núñez, J.; Mogollón, JM.; Rodríguez, AL.; Naranjo Ornedo, V. (2020). Deep Learning in Aeronautics: Air Traffic Trajectory Classification Based on Weather Reports. Springer. 148-155. https://doi.org/10.1007/978-3-030-62365-4_14 Pilot and technical outlook: Seattle. Boeing Commercial Airplanes, WA (2015) Wolter, C.A., Gore, B.F.: NASA/TM-2015-218480: A validated task analysis of the Single Pilot Operations concept, no. January 2015 (2015) Harris, D.: A human-centred design agenda for the development of single crew operated commercial aircraft. Aircr. Eng. Aerosp. Technol. 79(5), 518–526 (2007) Bailey, R.E., Kramer, L.J., Kennedy, K.D., Stephens, C.L., Etherington, T.J.: An assessment of reduced crew and single pilot operations in commercial transport aircraft operations. In: AIAA/IEEE Digital Avionics System Conference - Proceedings, vol. 2017-September, no. February 2018 (2017) Lachter, J., Brandt, S.L., Battiste, V., Ligda, S.V., Matessa, M., Johnson, W.W.: Toward single pilot operations: developing a ground station. In: Proceedings of International Conference on Human-Computer Interactive Aerospace, August (2014) Comerford, D., Brandt, S.L., Mogford, R.: NASA/CP - 2013–216513 NASA’s Single -Pilot Operations Technical Interchange Meeting: Proceedings and Findings, April, p. 89 (2013) Durand, N., Alliot, J.M., Médioni, F.: Neural nets trained by genetic algorithms for collision avoidance. Appl. Intell. 13(3), 205–213 (2000) Choi, S., Kim, Y.J., Briceno, S., Mavris, D.: Prediction of weather-induced airline delays based on machine learning algorithms. In: 2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC), pp. 1–6. IEEE, September 2016 Gui, G., Liu, F., Sun, J., Yang, J., Zhou, Z., Zhao, D.: Flight delay prediction based on aviation big data and machine learning. IEEE Trans. Veh. Technol. 69, 140–150 (2019) Liu, Y., Hansen, M.: Predicting aircraft trajectories: a deep generative convolutional recurrent neural networks approach. arXiv preprint arXiv:1812.11670 (2018) Shi, Z., Xu, M., Pan, Q., Yan, B., Zhang, H.: LSTM-based flight trajectory prediction. In: 2018 IEEE International Joint Conference on Neural Networks (IJCNN), pp. 1–8, July 2018 Bosson, C.D., Nikoleris, T.: Supervised learning applied to air traffic trajectory classification. In: 2018 AIAA Information Systems-AIAA Infotech@ Aerospace, p. 1637 (2018) FlightRadar24 website. https://www.flightradar24.com/ Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015) Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014) Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

Deep Learning in Aeronautics: Air Traffic Trajectory Classification Based on Weather Reports

Abstract