A Multiagent Approach to Managing Air Traffic Flow Public Deposited

http://ir.library.oregonstate.edu/concern/defaults/2227mq21k

This is the publisher’s final pdf. The published article is copyrighted by Springer and can be found at:  http://www.springerlink.com/content/1387-2532/. To the best of our knowledge, one or more authors of this paper were federal employees when contributing to this work.

Descriptions

Attribute NameValues
Creator
Abstract or Summary
  • Intelligent air traffic flow management is one of the fundamental challenges facing the Federal Aviation Administration (FAA) today. FAA estimates put weather, routing decisions and airport condition induced delays at 1,682,700 h in 2007 (FAA OPSNET Data, US Department of Transportation website, http://www.faa.gov/data_statistics/), resulting in a staggering economic loss of over $41 billion (Joint Economic Commission Majority Staff, Your flight has been delayed again, 2008). New solutions to the flow management are needed to accommodate the threefold increase in air traffic anticipated over the next two decades. Indeed, this is a complex problem where the interactions of changing conditions (e.g., weather), conflicting priorities (e.g., different airlines), limited resources (e.g., air traffic controllers) and heavy volume (e.g., over 40,000 flights over the US airspace) demand an adaptive and robust solution. In this paper we explore a multiagent algorithm where agents use reinforcement learning (RL) to reduce congestion through local actions. Each agent is associated with a fix (a specific location in 2D space) and has one of three actions: setting separation between airplanes, ordering ground delays or performing reroutes. We simulate air traffic using FACET which is an air traffic flow simulator developed at NASA and used extensively by the FAA and industry. Our FACET simulations on both artificial and real historical data from the Chicago and New York airspaces show that agents receiving personalized rewards reduce congestion by up to 80% over agents receiving a global reward and by up to 90% over a current industry approach (Monte Carlo estimation).
Resource Type
DOI
Date Available
Date Issued
Citation
  • Agogino, A., & Tumer, K. (2012). A multiagent approach to managing air traffic flow. AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS, 24(1), 1-25. doi: 10.1007/s10458-010-9142-5
Series
Keyword
Rights Statement
Funding Statement (additional comments about funding)
Publisher
Peer Reviewed
Language
Replaces
Additional Information
  • description.provenance : Submitted by Deanne Bruner (deanne.bruner@oregonstate.edu) on 2012-08-28T18:48:16Z No. of bitstreams: 1 TumerKaganMechIndMfgEngineeringMultiagentApproachManaging.pdf: 1101851 bytes, checksum: dd8fa7ca94fa0281151b542269f21a2f (MD5)
  • description.provenance : Approved for entry into archive by Deanne Bruner(deanne.bruner@oregonstate.edu) on 2012-08-28T18:49:31Z (GMT) No. of bitstreams: 1 TumerKaganMechIndMfgEngineeringMultiagentApproachManaging.pdf: 1101851 bytes, checksum: dd8fa7ca94fa0281151b542269f21a2f (MD5)
  • description.provenance : Made available in DSpace on 2012-08-28T18:49:31Z (GMT). No. of bitstreams: 1 TumerKaganMechIndMfgEngineeringMultiagentApproachManaging.pdf: 1101851 bytes, checksum: dd8fa7ca94fa0281151b542269f21a2f (MD5) Previous issue date: 2012-01

Relationships

In Administrative Set:
Last modified: 07/08/2017

Downloadable Content

Download PDF
Citations:

EndNote | Zotero | Mendeley

Items