Abstract
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.
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SESAR 2020, http://www.sesarju.eu/.
- 2.
NextGen, https://www.faa.gov/nextgen/.
- 3.
“Flightpath 2050” European Commission. Available Online: http://ec.europa.eu/transport/modes/air/doc/flightpath2050.pdf.
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Acknowledgements
This work is supported by the DART project, which has received funding from the SESAR Joint Undertaking under grant agreement No. 699299 under European Unions Horizon 2020 research and innovation programme. For more details, please see the DART project’s website, http://www.dart-research.eu.
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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: Berndt, J., Petta, P., Unland, R. (eds) Multiagent System Technologies. MATES 2017. Lecture Notes in Computer Science(), vol 10413. Springer, Cham. https://doi.org/10.1007/978-3-319-64798-2_15
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DOI: https://doi.org/10.1007/978-3-319-64798-2_15
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