Modeling Air Traffic Controller Performance in Highly Automated Environments

1989 ◽  
Vol 33 (2) ◽  
pp. 47-51 ◽  
Author(s):  
Elizabeth D. Murphy ◽  
Ray A. Reaux ◽  
Lisa J. Stewart ◽  
William D. Coleman ◽  
Kelly Harwood
Keyword(s):  
System Design ◽  
Traffic Control ◽  
Human Performance ◽  
The United States ◽  
Air Traffic ◽  
Automated System ◽  
Performance Variables ◽  
Design Changes ◽  
Controller Performance ◽  
Performance Dimensions

As increasing levels of automation are planned for the United States' air traffic control system, there is a need to assess planned system design changes for their potential effects on human performance. The model of controller performance developed by this work permits the comparison of prior and planned system transition states on several performance dimensions: perceptual, analytic, response, and resource management. Systematic predictions of performance provide a basis for identifying potential trouble spots in a planned system. The model can be employed to determine whether system design changes will improve controller performance without placing unreasonable demands on the controller's resources. It can be tailored to represent human performance variables and sources of resource demand in any complex automated system.

scholarly journals LEARNING INDIRECT ACTIONS IN COMPLEX DOMAINS: ACTION SUGGESTIONS FOR AIR TRAFFIC CONTROL

2009 ◽  
Vol 12 (04n05) ◽  
pp. 493-512 ◽  
Author(s):  
ADRIAN AGOGINO ◽  
KAGAN TUMER
Keyword(s):  
New York ◽  
Traffic Control ◽  
Transportation Systems ◽  
Air Traffic ◽  
Automated System ◽  
System Level ◽  
Research Issues ◽  
Air Traffic Controllers ◽  
Out Of The Loop ◽  
Series Of Experiments

Providing intelligent algorithms to manage the ever-increasing flow of air traffic is critical to the efficiency and economic viability of air transportation systems. Yet, current automated solutions leave existing human controllers "out of the loop" rendering the potential solutions both technically dangerous (e.g. inability to react to suddenly developing conditions) and politically charged (e.g. role of air traffic controllers in a fully automated system). Instead, this paper outlines a distributed agent-based solution where agents provide suggestions to human controllers. Though conceptually pleasing, this approach introduces two critical research issues. First, the agent actions are now filtered through interactions with other agents, human controllers and the environment before leading to a system state. This indirect action-to-effect process creates a complex learning problem. Second, even in the best case, not all air traffic controllers will be willing or able to follow the agents' suggestions. This partial participation effect will require the system to be robust to the number of controllers that follow the agent suggestions. In this paper, we present an agent reward structure that allows agents to learn good actions in this indirect environment, and explore the ability of those suggestion agents to achieve good system level performance. We present a series of experiments based on real historical air traffic data combined with simulation of air traffic flow around the New York city area. Results show that the agents can improve system-wide performance by up to 20% over that of human controllers alone, and that these results degrade gracefully when the number of human controllers that follow the agents' suggestions declines.

Free Flight: Human Factors Implications

1995 ◽  
Vol 39 (1) ◽  
pp. 98-102
Author(s):  
Daniel J. Garland ◽  
David W. Abbott ◽  
V. David Hopkin ◽  
John A. Wise ◽  
Russell A. Benel ◽  
...  
Keyword(s):  
Human Factors ◽  
Traffic Control ◽  
The United States ◽  
Air Traffic ◽  
Free Flight ◽  
Basic Notion ◽  
Traffic System ◽  
Airline Pilot ◽  
Traffic Control System ◽  
Traffic Systems

There is a real possibility that the air traffic control system in the United States will change radically in the next decade. One vision–“free flight” or “free routing”–is to move most of the responsibility for navigation and separation back to the cockpit and away from ground based air traffic systems. The basic notion of free flight is that each flight would be completely determined by the user, i.e. by some form of airline/pilot combination, and would not need to follow pre-defined airways or altitudes. The airlines would inform the air traffic system of each aircraft's intentions, but would not have to seek any prior air traffic approval. The job of the air traffic system would be to meet the user's requirements, but not to suggest what those requirements should be. However, the air traffic system would be expected to collaborate with the airlines to ensure the safe passage of flights and to intervene when aircraft separation requirements are jeopardized or violated. Such a system would bring with it dramatic changes in the roles of all the human members of the aviation system, and as such, would have significant human factors impacts. The goal of this panel will be to identify and discuss some of those issues.

Omar Human Performance Modeling in a Decision Support Experiment

1998 ◽  
Vol 42 (17) ◽  
pp. 1232-1236 ◽  
Author(s):  
Stephen Deutsch ◽  
Nichael Cramer
Keyword(s):  
Decision Support ◽  
Traffic Control ◽  
Human Performance ◽  
Human Subjects ◽  
Air Traffic ◽  
Performance Models ◽  
Future Directions ◽  
Air Traffic Controllers ◽  
System Experiment ◽  
Multiple Task

Human performance models that simulate the multiple task behaviors of the operators of complex systems are now being developed that can, with appropriate discretion, be used to complement the human players in real-world-like simulation environments. We have developed and used human performance models for an air traffic control simulation that was the basis for a decision support system experiment with human subjects. The experiment is briefly described and the roles played by the human performance models for air traffic controllers and flight crews are discussed. The theory that forms the foundation for the development of the human performance models, and the Operator Model Architecture developed to create the models are presented. Future directions for research based, in part, on the experiment results are outlined.

Human performance interfaces in air traffic control

2010 ◽  
Vol 41 (1) ◽  
pp. 123-129 ◽  
Author(s):  
Yu-Hern Chang ◽  
Chung-Hsing Yeh
Keyword(s):  
Traffic Control ◽  
Air Traffic Control ◽  
Human Performance ◽  
Air Traffic

scholarly journals Factor interaction influences on human performance in air traffic control: The need for a multifactorial model

Work ◽  
2012 ◽  
Vol 41 ◽  
pp. 159-166 ◽  
Author(s):  
Tamsyn Edwards ◽  
Sarah Sharples ◽  
John R. Wilson ◽  
Barry Kirwan
Keyword(s):  
Traffic Control ◽  
Air Traffic Control ◽  
Human Performance ◽  
Air Traffic ◽  
Multifactorial Model ◽  
Factor Interaction

An Explanatory Model for Influences of Air Traffic Control Task Parameters on Controller Work Pressure

1993 ◽  
Vol 37 (1) ◽  
pp. 108-112
Author(s):  
D. S. Bruce ◽  
Norman E. Freeberg ◽  
Donald A. Rock
Keyword(s):  
Facility Location ◽  
Traffic Control ◽  
Air Traffic Control ◽  
Time Of Day ◽  
Performance Outcomes ◽  
Traffic Volume ◽  
Air Traffic ◽  
Work Activity ◽  
Study Results ◽  
Controller Performance

Data were obtained in an operational air traffic control (ATC) setting from seven (7) FAA Air Route Traffic Control Centers (ARTCCs) for 65 air space sectors within those centers. Measures used for study analysis were obtained during 90 minute data collection sessions and included: (a) ATC system inputs in the form of time of day, facility location, air traffic volume and air traffic configurational complexity, (b) the presence of a single radar controller or a two-person controller team, (c) work activity parameters obtained as frequencies of occurrence for tasks involving communications, computer interactions, flight strip activities and handoffs, and (d) controller performance outcomes based on observed performance pressures exhibited by the controller. A hypothesized path (causal) model incorporating the above variables was constructed and tested for its explanatory value. Computations of direct effects within the model showed generally significant linkages between work activities and traffic volume, level of traffic complexity, and controller configuration (e.g. higher levels of traffic volume and complexity, and the presence of a controller team were associated with higher levels of task activity). Most consistently significant, however, in its causal linkages to work activity was air traffic complexity. This was the dominant predictor, by far, of rated controller performance pressure when all other variables - including traffic volume - were accounted for by the analysis method. The time of day and facility location variables were erratic in their effect and difficult to interpret. On the basis of the study results, it was recommended that further research focus on development of the application of air traffic complexity as a measurement construct of potentially unique importance; one which seems to have been little understood and generally neglected in the research literature.

Capturing Air Traffic Controller Expertise for Incorporation in Automated Air Traffic Control Systems

1988 ◽  
Vol 32 (16) ◽  
pp. 1031-1035
Author(s):  
Howard L. Bregman ◽  
Warren L. McCabe ◽  
William G. Sutcliffe
Keyword(s):  
Control Systems ◽  
Traffic Control ◽  
Air Traffic Control ◽  
Human Performance ◽  
Federal Aviation Administration ◽  
Air Traffic ◽  
Lessons Learned ◽  
Flight Safety ◽  
Air Traffic Controller ◽  
Air Traffic Controllers

Under Federal Aviation Administration (FAA) sponsorship, MITRE's Human Performance Assessment Group is contributing to the design of an expert system to support air traffic control. We are working closely with a team of expert, full-performance-level air traffic controllers to capture the formal and informal rules they use in maintaining flight safety and efficiency. This paper documents our approach to working with these experts, the results of using that approach, and a distillation of lessons learned.

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