Learning aircraft operational factors to improve aircraft climb prediction: A large scale multi-airport study

Author:

Alligier RichardORCID,Gianazza David

Publisher

Elsevier BV

Subject

Computer Science Applications,Transportation,Automotive Engineering,Civil and Structural Engineering

Reference45 articles.

1. Aircraft data aiming at predicting the trajectory. Data analysis report. Technical report;ADAPT2,2009

2. Alligier, R., Gianazza, D., Ghasemi Hamed, M., Durand, N., 2014. Comparison of two ground-based mass estimation methods on real data (regular paper). In: International Conference on Research in Air Transportation (ICRAT), Istambul, 26/05/2014-30/05/2014, page (on line). . ICRAT.

3. Learning the aircraft mass and thrust to improve the ground-based trajectory prediction of climbing flights;Alligier;Transport. Res. Part C: Emerg. Technol.,2013

4. Alligier, R., Gianazza, D., Durand, N., 2015. Machine learning applied to airspeed prediction during climb. In: Proceedings of the 11th USA/Europe Air Traffic Management R & D Seminar, Lisbon, Portugal.

5. Machine learning and mass estimation methods for ground-based aircraft climb prediction;Alligier;IEEE Trans. Intell. Transport. Syst.,2015

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