much more dense and complex than it is today. The present results show that under such conditions of saturated airspace, ATCos had difficulty both in detecting conflicts and in recognizing self-separating events in a timely manner. Controller workload also increased, as indexed both by subjective and secondary-task measures.
These findings extend those of previous studies examining controller performance under FF ( Endsley et al., 1997; Hilburn et al., 1997) by showing the impact of mature FF on controller workload and conflict- detection performance. Controllers in the present simulation study were required to monitor under very high traffic loads, did not have intent information, and were not provided any automation support tools. The use of these conditions was deliberate: system safety may be best evaluated under such worst-case scenarios. At the same time, although the results support previously expressed concerns about the ability of controllers to monitor airspace under mature FF, they also provide a benchmark against which performance improvements can be measured through the use of automation tools.
One example of such a tool is the conflict probe which could be used to improve potential conflict detection and allow the controller more time to consider available options. The present results, if replicated and extended to other airspace and traffic scenarios, could be used to set performance parameters for these and other automation support tools. For example, the advance notification time of 157 s we observed for self- separation maneuvers that were detected in the high traffic condition suggests a minimum notification time that a ground-based automated conflict probe should provide under mature FF. Of course, development of automated tools will also need to consider conflict resolution (in addition to conflict detection) times, as well as the integration of detection and resolution decisions between the air and the ground. Corker, Pisanich and Bunzo ( 1997) recently used a computational human performance model, Man Machine Integrated Design and Analysis System (MIDAS), to predict flight crew times to initiate evasive maneuvers to maintain separation under different conflict scenarios. For a scenario requiring ATC intervention (because one aircraft was not equipped for conflict detection), they obtained a mean time of 134 s for resolution of a 90 degree encounter geometry, but indicated that longer times might occur for shallower encounter angles. Human performance data such as these, as well as those of the present study, can be used to set design parameters for both air-based and ground-based conflict detection tools.
Higher levels of automation have been proposed as the means by which higher ATM capacity can be achieved without compromising safety. Recently, the NRC suggested that high levels of automation are best suited to tasks relating to information acquisition, integration and presentation to facilitate controller decision making ( Wickens et al., 1998). Proposed automated tools should be outlined with clear performance improvement goals and should be modeled and measured to ensure that those goals are attained within the decision making process of the ATCo. The present results provide a baseline assessment of mature FF against which the benefits of such ground-based automation support tools for the ATCo can be evaluated.
This work was supported by Grant No. NAG-2-1096 from NASA Ames Research Center, Moffett Field, California, USA. Kevin Corker was the technical monitor. The views presented here are those of the authors and do not necessarily reflect the views of the sponsor. We thank all of the controllers who participated in this study as well as Mike and Kimberly Connor from the National Aviation Research Institute (NARI) for their assistance in recruiting controllers.
Corker K., Pisanich G., & Bunzo M. ( 1997). Empirical and analytic studies of human/automation dynamics in airspace management for free flight. In Proceedings of the 10th International CEAS Conference on Free Flight, Amsterdam.