The present study is part of an ongoing effort to identify objective predictors of subjective air traffic controller workload. The study begins with a comparison of the salient variables governing en route controllers' perceptions of the performance capabilities of a sample of aircraft and the actual performance of the aircraft in the en route environment. A group of 24 Certified Professional Controllers (CPCs) from Kansas City (N= 17) and Boston (N= 7) en route centers provided estimates of cruising speed, climb, and descent rates for a sample of 24 aircraft types. A matrix of squared Euclidean distances derived from summary measures (i.e., means of estimated speed, climb, and descent rates) was used to construct a classical multidimensional scaling (CMDS) model representing controllers' perceptions of the performance capabilities of each aircraft type. A second matrix was derived from means of speed, climb, and descent rates for the same 24 aircraft types computed from a sample of live air traffic data collected from the Kansas City and Boston en route centers. This matrix was used to construct a second CMDS model representing actual aircraft performance. Interpretation of the dimensions of the CMDS model of ATC estimates suggested that Dimension 1 was related to engine type, whereas Dimension 2 was primarily associated with aircraft weight class. In the model of SAR data, both engine type and weight class were predominantly associated with Dimension 1. Results are used to develop a measure of aircraft mix (i.e., the mix of aircraft with different performance characteristics) to be added to a suite of controller activity and taskload measures.
Development of an Empirically-Based Index of Aircraft Mix
2003
14 pages
Report
Keine Angabe
Englisch
Air Transportation , Psychology , Personnel Management, Labor Relations & Manpower , Aeronautics , Human Factors Engineering , Performance(Engineering) , Air traffic controllers , Velocity , Measurement , Control systems , Predictions , Estimates , Scaling factor , Weight , Engines , Multisensors , Perception , Descent , Aircraft mix , Controlleter workload , Multidimensional scaling , Traffic complexity
Development Of An Empirically-Based Index of Aircraft Mix
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