Experimental studies of cognitively based air traffic control complexity metrics for future operational concepts

Abstract

New procedures and technologies of Air Traffic Control (ATC) under development in Next Generation Air Transportation System (NextGen) will change controllers' tasks, roles, and responsibilities. However, cognitive complexity will remain one of the limiting factors in future system's capacity and none of existing complexity metrics can be directly extended to evaluate cognitive complexity under future operational concepts. Therefore, complexity metrics, applicable to future operational concepts, need to be developed. This thesis developed the structure for a cognitively based complexity metric, Modified Aircraft Count (MAC). Cognitive complexity is decomposed based on in- individual aircraft complexity factors and sector level complexity factors. The complexity con- contributing of each aircraft is summed and adjusted by sector level complexity factors. Cognitive principles, such as controller strategies, may be incorporated in aircraft specific complexity factors and sector level complexity factors. To investigate complexity factors in Modified Aircraft Count, two simulations were developed to explore two proposed NextGen operational concepts, including Time-Based Control at a Metering Fix and Dynamic Route Structure Control. Two experiments were designed to evaluate controller performance and subjective workload under the simulated operational concepts. The Time-Based Control at a Metering Fix was found to have enhanced schedule conformance, reduced operational errors and lower perceived complexity. The Dynamic Route Structure Control introduced longer hand-o acceptance times, however, no other significant changes of controller performance and subjective workload were found.

(cont.) A new complexity probe technique was developed and applied in the two experiments to explore individual aircraft complexity factors in Modified Aircraft Count. In the new complexity probe, participants were asked to identify high complexity aircraft from the screen shot of a traffic situation they had experienced. It was shown to be an effective tool to assess aircraft specific complexity factors. Four complexity factors (proximity to other aircraft, membership of a standard ow, proximity to weather, and projected proximity to other aircraft) were examined by the relationship between 2 their corresponding observable factors and high complexity aircraft percentage. The chance of an aircraft being considered as of high complexity increased if the aircraft was closer to another aircraft, o the standard route structure, closer to the area impacted by weather, or more likely to be in a conflict in the future.