Learning Policies for Resolving Demand-Capacity Imbalances During Pre-tactical Air Traffic Management

Abstract

Summarization: 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. Παρουσιάστηκε στο: 15th German Conference on Multiagent System Technologie

Original document

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• http://link.springer.com/content/pdf/10.1007/978-3-319-64798-2_15,

http://dx.doi.org/10.1007/978-3-319-64798-2_15 under the license http://www.springer.com/tdm

• http://purl.tuc.gr/dl/dias/3F8984A1-1E9D-4EBC-BA1D-B282A8434E95,

10.1007/978-3-319-64798-2_15 https://link.springer.com/chapter/10.1007%2F978-3-319-64798-2_15 10.1007/978-3-319-64798-2_15 under the license License: http://creativecommons.org/licenses/by/4.0/

• https://link.springer.com/chapter/10.1007%2F978-3-319-64798-2_15,

https://dblp.uni-trier.de/db/conf/mates/mates2017.html#KravarisVSBCG17,

https://rd.springer.com/chapter/10.1007/978-3-319-64798-2_15,

https://academic.microsoft.com/#/detail/2738491172