Air traffic controller workload is considered to be a limiting factor for further air traffic growth. To reduce workload, increased automation levels and novel decision-support tools are being investigated. This Paper describes the adaptation and evaluation of a previously developed interface, called the Solution Space Diagram, in a route merging task. It portrays both constrained and unconstrained speed and heading combinations and enables the controller, by means of direct manipulation, to safely vector aircraft. The authors hypothesized that this interface enables controllers to use it in their own preferred way, supporting their skills and strategies, reducing their workload. A preliminary experiment was conducted in which 12 participants, grouped according to expertise level, controlled a sector and were faced with different levels of traffic in a route merging task. Results show that the interface aids in finding merging solutions faster; a significant reduction in the number of commands and in perceived workload was observed. The participants changed their strategy to perform less vectoring and issue route interceptions at an earlier stage, without affecting aircraft separation. These changes were also observed with the professional controllers, although they showed to be more conservative to the use of the diagram. This Paper justifies experimentation with a larger number of participants and in a setup of higher operational realism.

Through smart scheduling and triggering of automation support, adaptive automation has the potential to balance air traffic controller workload. The challenge in the design of adaptive automation systems is to decide how and when the automation should provide support. This paper describes the design of a novel mechanism for adaptively invoking automation support. Whereas most adaptive automation support systems are reactive in that they invoke automation support after controller workload has increased, the aim of the designed mechanism is to proactively trigger automation support prior to workload increases. To do this, the mechanism assesses the quality of air traffic controller's decisions. The designed adaptive automation system has been tested in a human-in-the-loop experiment. Results indicate that the adaptive support helps to increase efficiency and safety as compared to manual control. However, lower triggering thresholds (resulting in more frequent automation intervention) increased the frustration level of participants (as measured with NASA TLX) and decreased acceptance of the support.