S. Consortium, Milestone Deliverable D3: The ATM Target Concept, 2007.

H. Swenson, R. Barhydt, and M. Landis, Next Generation Air Transportation System (NGATS) Air Traffic Management (ATM)-Airspace Project, National Aeronautics and Space Administration, 2006.

X. Prats, V. Puig, J. Quevedo, and F. Nejjari, Multi-objective optimisation for aircraft departure trajectories minimising noise annoyance, special issue on Transportation Simulation Advances in Air Transportation Research, vol.18, issue.6, pp.975-989, 2010.

G. Chaloulos, E. Crück, and J. Lygeros, A simulation based study of subliminal control for air traffic management, Transportation Research Part C: Emerging Technologies, vol.18, pp.963-974, 2010.

N. Durand, J. Alliot, and J. Noailles, Automatic aircraft conflict resolution using Genetic Algorithms, Proceedings of the Symposium on Applied Computing, 1996.
URL : https://hal.archives-ouvertes.fr/hal-00937685

F. Drogoul, P. Averty, and R. Weber, Proceedings of the 8th USA/Europe Air Traffic Management R&D Seminar, 2009.

C. Vanaret, D. Gianazza, N. Durand, and J. Gotteland, Benchmarking conflict resolution algorithms, International Conference on Research in Air Transportation (ICRAT), 2012.
URL : https://hal.archives-ouvertes.fr/hal-00863090

V. Mouillet, User Manual for Base of Aircraft DATA (BADA) Rev.3.14, 2017.

R. Alligier, Predictive Distribution of the Mass and Speed Profile to Improve Aircraft Climb Prediction, 13th USA/Europe ATM R&D Seminar, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02545233

P. Martin and G. Mykoniatis, Study of the Acquisition of Data from Aircraft Operators to Aid Trajectory Prediction Calculation, 1998.

, Aircraft data aiming at predicting the trajectory. Data analysis report, 2009.

R. Coppenbarger, En route climb trajectory prediction enhancement using airline flight-planning information, Guidance, Navigation, and Control Conference and Exhibit, 1999.

J. Lopez-leones, M. A. Vilaplana, E. Gallo, F. A. Navarro, and C. Querejeta, The Aircraft Intent Description Language: A key enabler for air-ground synchronization in Trajectory-Based Operations, IEEE/AIAA 26th Digital Avionics Systems Conference, 2007.

J. Lopes-leonés, The Aircraft Intent Description Language, 2007.

C. Schultz, D. Thipphavong, and H. E. , Adaptive Trajectory Prediction Algorithm for Climbing Flights, AIAA Guidance, Navigation, and Control Conference, 2012.

D. P. Thipphavong, C. A. Schultz, A. G. Lee, C. , and S. H. , Adaptive Algorithm to Improve Trajectory Prediction Accuracy of Climbing Aircraft, Journal of Guidance, Control, and Dynamics, vol.36, issue.1, pp.15-24, 2013.

Y. S. Park and D. P. Thipphavong, Performance of an Adaptive Trajectory Prediction Algorithm for Climbing Aircraft, 2013 Aviation Technology, Integration, and Operations Conference, vol.08, 2013.

R. Alligier, D. Gianazza, M. G. Hamed, D. , and N. , Comparison of Two Ground-based Mass Estimation Methods on Real Data, International Conference on Research in Air Transportation (ICRAT, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01002401

J. Sun, J. Ellerbroek, and J. Hoekstra, Modeling and inferring aircraft takeoff mass from runway ADS-B data, 7th International Conference on Research in Air Transportation, 2016.

M. Uzun and E. Koyuncu, Data-Driven Trajectory Uncertainty Quantification For Climbing Aircraft To Improve Ground-Based Trajectory Prediction, Anadolu Üniversitesi Bilim Ve Teknoloji Dergisi A-Uygulamal? Bilimler ve Mühendislik, vol.18, pp.323-345, 2017.

J. Bronsvoort, G. Mcdonald, M. Paglione, C. M. Young, J. Boucquey et al., Real-Time Trajectory Predictor Calibration through Extended Projected Profile Down-Link, Europe Air Traffic Management Research and Development Seminar, 2015.

R. Alligier, D. Gianazza, D. , and N. , Learning the aircraft mass and thrust to improve the ground-based trajectory prediction of climbing flights, Transportation Research Part C: Emerging Technologies, vol.36, pp.45-60, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00907651

J. Sun, J. Ellerbroek, and J. Hoekstra, Proceedings of the 12th USA/Europe Air Traffic Management Research and Development Seminar, 2017.

J. Sun, J. Ellerbroek, and J. M. Hoekstra, Aircraft initial mass estimation using Bayesian inference method, Transportation Research Part C: Emerging Technologies, vol.90, pp.59-73, 2018.

J. Sun, H. A. Blom, J. Ellerbroek, and J. M. Hoekstra, Aircraft Mass and Thrust Estimation Using Recursive Bayesian Method, 2018.

Y. S. Chati and H. Balakrishnan, ATM Seminar, 12th USA/Europe Air Traffic Management R&D Seminar, 2017.

Y. S. Chati and H. Balakrishnan, Modeling of Aircraft Takeoff Weight Using Gaussian Processes, Journal of Air Transportation, vol.26, issue.2, pp.70-79, 2018.

J. Hensman, A. Matthews, and Z. Ghahramani, Scalable Variational Gaussian Process Classification, Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research, vol.38, pp.351-360, 2015.

R. Alligier and D. Gianazza, Learning aircraft operational factors to improve aircraft climb prediction: A large scale multiairport study, Transportation Research Part C: Emerging Technologies, vol.96, pp.72-95, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01878615

M. Schäfer, M. Strohmeier, V. Lenders, I. Martinovic, W. et al., Bringing up OpenSky: A large-scale ADS-B sensor network for research, IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks, pp.83-94, 2014.

E. E. Centre, Coverage of 2016 European Air Traffic for the Base of Aircraft Data (BADA) versions 3.14 & 4.2, EUROCONTROL, 2017.

J. Sun, World Aircraft Database, 2017.

T. Hastie, R. Tibshirani, and J. H. Friedman, The Elements of Statistical Learning, 2001.

C. M. Bishop, Pattern recognition and machine learning, vol.1, 2006.

C. E. Rasmussen, Gaussian Processes in Machine Learning, pp.63-71, 2004.

Y. Gal and Z. Ghahramani, Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning, Proceedings of The 33rd International Conference on Machine Learning, Proceedings of Machine Learning Research, vol.48, pp.1050-1059, 2016.

B. Lakshminarayanan, A. Pritzel, and C. Blundell, Simple and scalable predictive uncertainty estimation using deep ensembles, Advances in Neural Information Processing Systems, pp.6402-6413, 2017.

A. L. Maas, A. Y. Hannun, and A. Y. Ng, Rectifier nonlinearities improve neural network acoustic models, ICML Workshop on Deep Learning for Audio, Speech and Language Processing, 2013.

A. De-brébisson, É. Simon, A. Auvolat, P. Vincent, and Y. Bengio, Artificial neural networks applied to taxi destination prediction, Proceedings of the 2015th International Conference on ECML PKDD Discovery Challenge, vol.1526, pp.40-51, 2015.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting, The Journal of Machine Learning Research, vol.15, issue.1, pp.1929-1958, 2014.

S. Ioffe and C. Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, International Conference on Machine Learning, pp.448-456, 2015.

I. Loshchilov and F. Hutter, Decoupled Weight Decay Regularization, International Conference on Learning Representations, 2019.

L. N. Smith, Cyclical learning rates for training neural networks, pp.464-472, 2017.

J. Bergstra and Y. Bengio, Random search for hyper-parameter optimization, Journal of Machine Learning Research, vol.13, pp.281-305, 2012.

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang et al., Automatic Differentiation in PyTorch, NeurIPS Autodiff Workshop, 2017.

I. Shavitt, E. S. Segal, H. Bengio, H. Wallach, K. Larochelle et al., Regularization Learning Networks: Deep Learning for Tabular Datasets, Advances in Neural Information Processing Systems, vol.31, pp.1386-1396, 2018.

E. Casado, M. L. Civita, M. Vilaplana, and E. W. Mcgookin, Quantification of Aircraft Trajectory Prediction Uncertainty Using Polynomial Chaos Expansions, IEEE/AIAA 36th Digital Avionics Systems Conference (DASC), 2017.