Methodology for Correlation of ATC Controller Workload and Errors: Phase II Final Report
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Methodology for Correlation of ATC Controller Workload and Errors: Phase II Final Report

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      Phase II Final report; Oct 87- Nov 89
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    • Abstract:
      The purpose of the research and development conducted in Phase II was to provide the Federal Aviation Administration (FAA) with a valid method of evaluating design changes early in the system development life cycle. This method is embodied in PA TCAM, the Predictive Air Traffic Control Analysis Model, the primary product of Phase II. PATCAM is an engineering tool for use in predicting the workload and performance of air traffic controllers in future transition states of the National Airspace System.

      Phase II research and development activities focused on designing and implementing a powerful demonstration of PATCAM's capabilities. Research activities included model enhancement and validation, development of controller task networks, and construction of databases representing validated operational activities for two future NAS transition states: the Advanced Automation System (AAS) and an AAS enhancement, the Advanced En Route Air Traffic Control (AERA 2) environment. The Phase II research led to the introduction of a new construct, the mental interface, to represent important cognitive structures that have not been previously considered in analyses of controller workload and performance.

      PATCAM permits the comparison of predicted demands on the controller and consequent controller performance, as a function of available skills and cognitive resources. Comparisons can be made on the basis of simulated "critical incidents," which appear in the comparable AAS and AERA 2 scenarios. Because the AAS and AERA 2 operations concepts have evolved beyond the baselines used for PATCAM development, results of the AAS and AERA 2 comparisons are presented for proof -of-concept purposes only. For example, results indicate that the simulated AERA 2 controller can deal with significantly more A TC situations than can the simulated AAS controller in the same amount of time, with no appreciable increase in task-induced demand.

      Phase II has demonstrated the feasibility of developing a validated, predictive

      tool for evaluating the workload and performance effects of planned system capabilities. Further research is recommended to expand the tool's scope and usefulness in identifying potential trouble spots for correction prior to design and fabrication of increasingly automated air traffic control systems.

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