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.

. Sesar-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, Transportation Research Part C, 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, Special issue on Transportation Simulation Advances in Air Transportation Research, vol.18, issue.6, 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, Philadelphia, 1996.
URL : https://hal.archives-ouvertes.fr/hal-00937685

F. Drogoul, P. Averty, and R. Weber, Erasmus strategic deconfliction to benefit sesar, 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.

P. Martin and G. Mykoniatis, Study of the acquisition of data from aircraft operators to aid trajectory prediction calculation, 1998.

, ADAPT2. aircraft data aiming at predicting the trajectory. data analysis report, 2009.

R. A. Coppenbarger, Climb trajectory prediction enhancement using airline flight-planning information, AIAA Guidance, Navigation, and Control Conference, 1999.

J. López-leonés, 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, Proceedings of the 26th IEEE/AIAA Digital Avionics Systems Conference. DASC, 2007.

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

A. Charles, D. Schultz, H. Thipphavong, and . Erzberger, Adaptive trajectory prediction algorithm for climbing flights, AIAA Guidance, Navigation, and Control (GNC) Conference, 2012.

P. David, C. A. Thipphavong, A. G. Schultz, S. Lee, and . Chan, Adaptive algorithm to improve trajectory prediction accuracy of climbing aircraft, Journal of Guidance, Control, and Dynamics, vol.36, issue.1, pp.15-24, 2012.

Y. S. Park and D. P. Thipphavong, Performance of an Adaptive Trajectory Prediction Algorithm for Climbing Aircraft, p.8, 2013.

R. Alligier, D. Gianazza, M. G. Hamed, and N. Durand, Comparison of Two Groundbased Mass Estimation Methods on Real Data
URL : https://hal.archives-ouvertes.fr/hal-01002401

, International Conference on Research in Air Transportation (ICRAT, 2014.

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, 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, Eleventh USA/Europe Air Traffic Management Research and Development Seminar, 2015.

R. Alligier, D. Gianazza, and N. Durand, 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, Bayesian inference of aircraft initial mass, Proceedings of the 12th USA/Europe Air Traffic Management Research and Development Seminar. FAA/EUROCONTROL, 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, A. P. Henk, J. Blom, J. Ellerbroek, and . Hoekstra, Aircraft mass and thrust estimation using recursive bayesian method, 2018.

S. Yashovardhan, H. Chati, and . Balakrishnan, Statistical modeling of aircraft takeoff weight, 2017.

S. Yashovardhan, H. Chati, and . Balakrishnan, Modeling of aircraft takeoff weight using gaussian processes, Journal of Air Transportation, pp.1-10, 2018.

R. Alligier and D. Gianazza, Learning aircraft operational factors to improve aircraft climb prediction: A large scale multi-airport 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, and M. Wilhelm, Bringing Up OpenSky: A Large-scale ADS-B Sensor Network for Research, Proceedings of the 13th International Symposium on Information Processing in Sensor Networks, IPSN '14, pp.83-94, 2014.

. Eurocontrol-experimental-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. Springer Series in Statistics, 2001.

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

C. E. Rasmussen, Gaussian processes in machine learning, Advanced lectures on machine learning, pp.63-71, 2004.

Y. Gal and Z. Ghahramani, Dropout as a bayesian approximation: Representing model uncertainty in deep learning, international conference on machine learning, 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.

L. Andrew, . Maas, Y. Awni, A. Hannun, and . 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.

N. Leslie and . Smith, Cyclical learning rates for training neural networks, Applications of Computer Vision (WACV), 2017 IEEE Winter Conference on, pp.464-472, 2017.

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

I. Shavitt and E. Segal, Regularization learning networks: Deep learning for tabular datasets, Advances in Neural Information Processing Systems, vol.31, pp.1386-1396, 2018.