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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 hours in 2007, resulting in a staggering economic loss of over $41 Billion. 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 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).
@article{tumer-agogino_jaamas12,
author = {A. K. Agogino and K. Tumer},
title = {A Multiagent Approach to Managing Air Traffic Flow},
journal = {Autonomous Agents and MultiAgent Systems},
Volume = {24},
Pages = {1-25},
abstract ={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 hours in 2007, resulting in a staggering economic loss of over $41 Billion. 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 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).},
bib2html_pubtype = {Journal Articles},
bib2html_rescat = {Air Traffic Control, Multiagent Systems, Traffic and Transportation},
year = {2012}
}
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