A Multi-Agent Simulation of Collaborative Air Traffic Flow Management

A Multi-Agent Simulation of Collaborative Air Traffic Flow Management

Shawn R. Wolfe (NASA Ames Research Center, USA), Peter A. Jarvis (NASA Ames Research Center, USA), Francis Y. Enomoto (NASA Ames Research Center, USA), Maarten Sierhuis (USRA-RIACS/Delft University of Technology, The Netherlands and NASA Ames Research Center, USA) and Bart-Jan van Putten (USRA-RIACS/Utrecht University, Netherlands)
Copyright: © 2009 |Pages: 25
DOI: 10.4018/978-1-60566-226-8.ch018
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Abstract

Today’s air traffic management system is not expected to scale to the projected increase in traffic over the next two decades. Enhancing collaboration between the controllers and the users of the airspace could lessen the impact of the resulting air traffic flow problems. The authors summarize a new concept that has been proposed for collaborative air traffic flow management, the problems it is meant to address, and our approach to evaluating the concept. The authors present their initial simulation design and experimental results, using several simple route selection strategies and traffic flow management approaches. Though their model is still in an early stage of development, these results have revealed interesting properties of the proposed concept that will guide their continued development, refinement of the model, and possibly influence other studies of traffic management elsewhere. Finally, they conclude with the challenges of validating the proposed concept through simulation and future work.
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Introduction

Air traffic in the United States of America (U.S.A.) is forecasted to double or triple by the year 2025 (Pearce, 2006). Recent simulations (Mukherjee, Grabbe, & Sridhar, 2008) of this increase in demand using current air traffic management techniques yielded an increase in average delay per flight from four minutes to over five hours – a clearly unacceptable situation. Accordingly, the National Aeronautics and Space Administration (NASA) is currently exploring several new concepts that may reduce or alleviate air traffic problems. One such concept is Collaborative Air Traffic Flow Management (CATFM), which seeks to lessen the impact on airspace user operations rather than eliminate the problem. Today in the U.S.A., the Federal Aviation Administration (FAA) makes the bulk of Air Traffic Flow Management (ATFM) decisions with only limited consultation with the airlines. In CATFM, the airspace users are given more opportunities to express their preferences, choose among options, and take proactive actions. It is presumed that this will result in decreased workload for the FAA, increased airline satisfaction, and more efficient traffic flow management (Odoni, 1987).

Several questions arise when evaluating if the CATFM concept will work in the future environment. Will the airlines take advantage of new opportunities for action, or will they be passive and let the FAA continue to solve traffic problems independently? Will increasing airline involvement decrease the FAA’s workload? Will the options available to the airlines enable them to substantially increase the efficiency of their operations, in particular when many factors still remain out of their control? Will the uncoordinated actions of individual airlines increase the efficiency of the system as a whole, even though each airline is only concerned with their own operations (Waslander, Raffard, & Tomlin, 2008)? Might potential efficiency gains be offset by the actions of rogue operators, who purposely seek to interfere with the operations of a competitor (Hardin, 1968)?

Given that the CATFM concept involves many independent entities with their own beliefs and desires, we feel that the first step to answering some of these questions is through agent-based modeling and simulation. Our goal is to build a simulation of CATFM so that its strengths and weaknesses can be evaluated long before more costly human–in-the-loop simulations or limited field deployments are attempted (Wambsganss, 1996). Our simulation is in an early stage of development, but we have already found several interesting and important properties of CATFM (presented in our conclusions).

Though our study is certainly most relevant to air traffic, certain aspects are relevant to other forms of traffic as well. Our methodology can be applied to any concept of operations in these domains. Many of the basic concepts (e.g., choosing routes, traffic congestion, independent and uncoordinated agent actions) are the same and the overall structure is similar. Nonetheless, there are important differences. An aircraft’s airborne speed must remain in a narrow range: significant speed increases are usually unachievable; slower speeds can produce stalls; and halting is impossible. This greatly constrains the actions that are available, and is further limited by the amount of fuel onboard (which is minimized to reduce operating costs).

ATFM generally has more centralized control than other forms of traffic management: In contrast, CATFM increases information sharing and distributes some elements of decision making. Finally, a significant portion of air traffic is comprised of fleets (i.e., airlines) – essentially allied pilots who are interested in cooperating for the common good of the company.

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