S. J. Undertaking, European ATM master plan, Tech. Rep, 2015.

S. Lloyd, Least squares quantization in PCM, IEEE Transactions on Information Theory, vol.28, issue.2, pp.129-137, 1982.
DOI : 10.1109/TIT.1982.1056489

T. Zhang, R. Ramakrishnan, and M. Livny, Birch: an efficient data clustering method for very large databases, ACM Sigmod Record, pp.103-114, 1996.

M. Ester, H. Kriegel, J. Sander, and X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, Kdd, vol.96, issue.34, pp.226-231, 1996.

M. Ankerst, M. M. Breunig, H. Kriegel, and J. Sander, Optics: ordering points to identify the clustering structure, ACM Sigmod record, pp.49-60, 1999.

M. C. Murça, R. Delaura, R. Hansman, R. Jordan, T. Reynolds et al., Trajectory clustering and classification for characterization of air traffic flows, 2016.

M. Gariel, A. N. Srivastava, and E. Feron, Trajectory Clustering and an Application to Airspace Monitoring, IEEE Transactions on Intelligent Transportation Systems, vol.12, issue.4, pp.1511-1524, 2011.
DOI : 10.1109/TITS.2011.2160628

J. Lee, J. Han, and K. Whang, Trajectory clustering, Proceedings of the 2007 ACM SIGMOD international conference on Management of data , SIGMOD '07, pp.593-604, 2007.
DOI : 10.1145/1247480.1247546

A. T. Nguyen, Identification of air traffic flow segments via incremental deterministic annealing clustering, 2012.

M. Enriquez, Identifying temporally persistent flows in the terminal airspace via spectral clustering, Tenth USA/Europe Air Traffic Management Research and Development Seminar (ATM2013), 2013.

M. K. Mahrsi and F. Rossi, Graph-Based Approaches to Clustering Network-Constrained Trajectory Data, International Workshop on New Frontiers in Mining Complex Patterns, pp.124-137, 2012.
DOI : 10.1007/978-3-642-37382-4_9
URL : https://hal.archives-ouvertes.fr/hal-00737457

S. Puechmorel and F. Nicol, Entropy Minimizing Curves with Application to Flight Path Design and Clustering, Entropy, vol.18, issue.9, p.337, 2016.
DOI : 10.4171/JEMS/37
URL : https://hal.archives-ouvertes.fr/hal-01349675

R. J. Campello, D. Moulavi, and J. Sander, Density-Based Clustering Based on Hierarchical Density Estimates, Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp.160-172, 2013.
DOI : 10.1007/978-3-642-37456-2_14

B. Guillouet, Apprentissage statistique: application au trafic routieràroutier`routierà partir de données structurées et aux données massives, 2016.

L. Mcinnes, J. Healy, and S. Astels, hdbscan: Hierarchical density based clustering Available: https, The Journal of Open Source Software, vol.2, issue.2017

. Eurocontrol and . Ddr, Available: http://www.eurocontrol An iterative procedure for the polygonal approximation of plane curves, Computer graphics and image processing, vol.1, issue.3, pp.244-256, 1972.

D. H. Douglas and T. K. Peucker, ALGORITHMS FOR THE REDUCTION OF THE NUMBER OF POINTS REQUIRED TO REPRESENT A DIGITIZED LINE OR ITS CARICATURE, Cartographica: The International Journal for Geographic Information and Geovisualization, vol.10, issue.2, pp.112-122, 1973.
DOI : 10.3138/FM57-6770-U75U-7727

F. N. Fritsch and R. E. Carlson, Monotone Piecewise Cubic Interpolation, SIAM Journal on Numerical Analysis, vol.17, issue.2, pp.238-246, 1980.
DOI : 10.1137/0717021

D. Kahaner, C. Moler, and S. Nash, Numerical methods and software, 1989.