N. Durand, J. M. Alliot, and G. Granger, A statistical analysis of the influence of vertical and ground speed errors on conflict probe, Proceedings of ATM2001, 2001.

X. Prats, V. Puig, J. Quevedo, and F. Nejjari, Multi-objective optimisation for aircraft departure trajectories minimising noise annoyance, Transportation Research Part C: Emerging Technologies, vol.18, issue.6, pp.975-989, 2010.
DOI : 10.1016/j.trc.2010.03.001

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, issue.6, pp.963-974, 2010.
DOI : 10.1016/j.trc.2010.03.002

K. James, . Kuchar, C. Lee, and . Yang, A review of conflict detection and resolution modeling methods. Intelligent Transportation Systems, IEEE Transactions on, vol.1, issue.4, pp.179-189, 2000.

L. Pallottino, M. Eric, A. Feron, and . Bicchi, Conflict resolution problems for air traffic management systems solved with mixed integer programming. Intelligent Transportation Systems, IEEE Transactions on, vol.3, issue.1, pp.3-11, 2002.

J. M. Alliot, H. Gruber, and M. Schoenauer, Genetic algorithms for solving ATC conflicts, Proceedings of the Ninth Conference on Artificial Intelligence Application. IEEE, 1992.

N. Durand, J. M. Alliot, and J. Noailles, Automatic aircraft conflict resolution using genetic algorithms, Proceedings of the 1996 ACM symposium on Applied Computing , SAC '96, 1996.
DOI : 10.1145/331119.331195
URL : https://hal.archives-ouvertes.fr/hal-00937685

N. Durand and J. Alliot, Ant colony optimization for air traffic conflict resolution, 8th USA/Europe Air Traffic Management Research and Developpment Seminar, 2009.
URL : https://hal.archives-ouvertes.fr/hal-01293554

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

R. Alligier, D. Gianazza, and N. Durand, Machine learning and mass estimation methods for ground-based aircraft climb prediction. Intelligent Transportation Systems, IEEE Transactions on, vol.16, issue.6, pp.3138-3149, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01181173

R. Alligier, D. Gianazza, and N. Durand, Machine learning applied to airspeed prediction during climb, Proceedings of the 11th USA, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01168664

M. Steven, R. Green, and . Vivona, Field evaluation of descent advisor trajectory prediction accuracy, AIAA Guidance, Navigation and Control Conference, 1996.

S. Green, R. Vivona, M. Grace, and T. Fang, Field evaluation of descent advisor trajectory prediction accuracy for en-route clearance advisories, Guidance, Navigation, and Control Conference and Exhibit, 1998.
DOI : 10.2514/6.1998-4479

L. Stell, Predictability of Top of Descent Location for Operational Idle-Thrust Descents, 10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, 2010.
DOI : 10.2514/6.2010-9116

L. Stell, Prediction of top of descent location for idle-thrust descents, 9th USA, 2011.

L. Stell, J. Bronsvoort, and G. Mcdonald, Regression analysis of top of descent location for idle-thrust descents, Proceedings of the 10th USA/Europe Air Traffic Management R & D Seminar, 2013.

C. Eurocontrol-experimental, User manual for base of aircarft data (bada) rev.3.13, p.2015

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

C. Bishop, Pattern recognition and machine learning, 2006.

N. Vladimir, A. Vapnik, and . Ya, Chervonenkis. The necessary and sufficient conditions for consistency of the method of empirical risk minimization. Pattern Recogn, Image Anal, vol.1, issue.3, pp.284-305, 1991.

N. Vladimir and . Vapnik, The nature of statistical learning theory, 1995.

A. Blum, A. Kalai, and J. Langford, Beating the hold-out, Proceedings of the twelfth annual conference on Computational learning theory , COLT '99, pp.203-208, 1999.
DOI : 10.1145/307400.307439

R. Kohavi, A study of cross-validation and bootstrap for accuracy estimation and model selection, pp.1137-1143, 1995.

J. Fox, Applied regression analysis, linear models, and related methods, 1997.

C. R. Rao and H. Toutenburg, Linear Models: Least Squares and Alternatives, 1999.

E. Arthur, R. W. Hoerl, and . Kennard, Ridge regression: Biased estimation for nonorthogonal problems, Technometrics, vol.12, issue.1, pp.55-67, 1970.

C. M. Bishop, Neural networks for pattern recognition, pp.0-198, 1996.

B. D. Ripley, Pattern recognition and neural networks, pp.0-521, 1996.
DOI : 10.1017/CBO9780511812651

J. H. Friedman, Stochastic gradient boosting, Computational Statistics & Data Analysis, vol.38, issue.4, pp.367-378, 2002.
DOI : 10.1016/S0167-9473(01)00065-2

J. H. Friedman, machine., The Annals of Statistics, vol.29, issue.5, pp.1189-1232, 2000.
DOI : 10.1214/aos/1013203451

L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees. Statistics/Probability Series, 1984.

G. Ridgeway, Generalized boosted models: A guide to the gbm package, Update, vol.1, issue.1, 2007.

R. D. Alligier-received-his-ph, degree in Computer Science from theInstitut National Polytechnique de Toulouse" (INPT), his engineers degrees (IEEAC, 2010) from the french university of civil aviation (ENAC) and his M.Sc. (2010) in computer science from the University of Toulouse. He is currently assistant professor at the ENAC in Toulouse, France. David Gianazza received his two engineer degrees (1986, 1996) from the french university of civil aviation (ENAC) and his M, Computer Science from the "Institut National Polytechnique de Toulouse, 1996.