IDEAS home Printed from https://ideas.repec.org/a/ids/ijavim/v1y2012i3p145-161.html
   My bibliography  Save this article

Reinforcement learning agents to tactical air traffic flow management

Author

Listed:
  • Antonio Marcio Ferreira Crespo
  • Li Weigang
  • Alexandre Gomes de Barros

Abstract

Air traffic flow management (ATFM) is of crucial importance for the airspace control system, due to two factors: first, the impact of ATFM on air traffic control, including inherent safety implications on air operations; second, the possible consequences of ATFM measures on airport operations. Thus, it is imperative to establish procedures and develop systems that help traffic flow managers to take optimal actions. In this context, this work presents a comparative study of ATFM measures generated by a computational agent based on artificial intelligence (reinforcement learning). The goal of the agent is to establish delays upon takeoff schedules of aircraft departing from certain terminal areas so as to avoid congestion or saturation in the air traffic control sectors due to a possible imbalance between demand and capacity. The paper includes a case study comparing the ATFM measures generated by the agent autonomously and measures generated taking into account the experience of human traffic flow managers. The experiments showed satisfactory results.

Suggested Citation

  • Antonio Marcio Ferreira Crespo & Li Weigang & Alexandre Gomes de Barros, 2012. "Reinforcement learning agents to tactical air traffic flow management," International Journal of Aviation Management, Inderscience Enterprises Ltd, vol. 1(3), pages 145-161.
    • Handle: RePEc:ids:ijavim:v:1:y:2012:i:3:p:145-161
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=45736
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ids:ijavim:v:1:y:2012:i:3:p:145-161. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=289 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.