In this work we propose and investigate the use of collaborative reinforcement learning methods for resolving demand-capacity imbalances during pre-tactical Air Traffic Management. By so doing, we also initiate the study of data-driven techniques for predicting multiple correlated aircraft trajectories; and, as such, respond to a need identified in contemporary research and practice in air-traffic management. Our simulations, designed based on real-world data, confirm the effectiveness of our methods in resolving the demand-capacity problem, even in extremely hard scenarios.
CITATION STYLE
Kravaris, T., Vouros, G. A., Spatharis, C., Blekas, K., Chalkiadakis, G., & Garcia, J. M. C. (2017). Learning policies for resolving demand-capacity imbalances during pre-tactical air traffic management. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10413 LNAI, pp. 238–255). Springer Verlag. https://doi.org/10.1007/978-3-319-64798-2_15
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