Addressing Deficiencies in Human Performance Models of Air Traffic Control

Yul Kwon, Purdue University


The development of a broad array of human performance models varying in complexity has greatly enhanced the ability to examine human-system interactions and evaluate potential benefits and drawbacks of introducing new operational concepts and technologies envisioned for the next-generation air transportation system. However, despite the benefits, there are deficiencies in existing human performance models that affect the accuracy of the predictions being made from simulation platforms. In this dissertation, I attempted to address two research gaps that can contribute to reducing model uncertainty associated with human performance models in fast-time simulation of the air traffic control system. The first study of the dissertation attempted to systematically investigate how air traffic controllers alter flight trajectories in response to predicted aircraft pair conflict using recorded air traffic control data. A deterministic simulation was performed with open-loop fight trajectories to identify aircraft pairs that were expected to engage in aircraft conflict by calculating the separation distance between aircraft. Then, a rule-based algorithm was developed and used to automatically identify and classify operationally significant deviations in recorded transportation data by analyzing the actual positions of an aircraft to its filed flight plan when the aircraft trajectories have been identified as having encounters in aircraft conflict. The results of the study suggested evidence of controllers preferring the use of horizontal maneuvers as opposed to the vertical maneuvers and a preference towards applying direct routing over path-stretch maneuvers as a means to resolve conflicts. The second study of the dissertation attempted to determine if adding cognitive multitasking processes to computational task-analytic models of human performance result in practically different predictions of system or human performance, specifically task-relevant performance metrics and workload. Event-driven discrete simulations were run using either a simple task analytic model, a more detailed GOMS task-analytic model, and a multi-tasking QN-MHP task analytic model; these models' predictions of the task-relevant performance metrics and workload were then compared to see if the models, which differ in terms of complexity and development effort, made meaningfully different predictions. The overall results suggest that little can be gained by adding cognitive multitasking processes to computational task-analytic models in measuring task-relevant performance metrics and workload.




Landry, Purdue University.

Subject Area

Industrial engineering

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