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


Ontology type: schema:Chapter     


Chapter Info

DATE

2017-07-28

AUTHORS

Theocharis Kravaris , George A. Vouros , Christos Spatharis , Konstantinos Blekas , Georgios Chalkiadakis , Jose Manuel Cordero Garcia

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. More... »

PAGES

238-255

References to SciGraph publications

  • 2012-01. A multiagent approach to managing air traffic flow in AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-64798-2_15

    DOI

    http://dx.doi.org/10.1007/978-3-319-64798-2_15

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1090965528


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