Various methods have been introduced in the past in efforts to optimize airspace sector design and the allocation of air traffic controllers. This is done with the aim to accommodate growth, increase productivity and most importantly to ensure safety of air traffic. To accomplish this, a more comprehensive understanding of human workload, especially that of the controllers involved, is required. In Air Traffic Control (ATC), there exists a maximum number of aircraft per sector that the Air Traffic Controller (ATCO) is assumed to be capable of controlling simultaneously. The maximum controllable traffic is gathered based on experimentation and subjective assessments of controller workload, which are sector specific. This threshold is not to be exceeded in order to maintain a reasonable and sustainable level of workload. However, a sector complexity metric based on the maximum number of aircraft does not consider the dynamic nature of air traffic, thus limiting the possibility of accommodating the growth of air traffic. Consequently, to better support strategic decisions that need information on ATC workload, we need better measures than just the number of aircraft. Metrics, for example the Dynamic Density (DD) that use a weighted combination of static and dynamic airspace properties, such as the number of aircraft flying through a sector, the ratio of climbing, cruising and descending aircraft, the horizontal proximity between aircraft et cetera, have been constructed and proposed as a sector complexity measure. The proposed weightings are determined through regression analysis on expert judgement for a particular sector design. As a result, these metrics become highly dependent on sector and operator-centered factors and therefore not uniformly applicable to a wider range of operators and sector designs. A careful calibration would then be needed to tailor the measure to each individual operator and also to the considered sector. In an effort to find a more objective measure of sector complexity and a predictor of workload, this thesis investigates a constraint-based measure based on the Solution Space Diagram (SSD). In essence, the SSD is a method to observe aircraft restrictions and opportunities to resolve air traffic conflicts in both the speed and heading dimensions. The SSD can be described as the available control area for the controlled aircraft in respect to other observed aircraft within the vicinity. The construction of the SSD is based on the projection of the ‘zone of conflict’ of the observed aircraft where the key constraint is the 5 NM separation minimum between aircraft. When considering the SSD for any individual aircraft, all neighboring aircraft introduce a ‘no-go area’ or ‘zone of conflict’ on the SSD. Intrusion of this zone is called a conflict, or, loss of separation. Looking at the results of the numerous off-line and real-time human-in-the-loop experiments, the proposed SSD metric shows a promising prospect of being an objective measure of sector complexity and a viable subjective workload predictor. However, these results are based on specific experiment settings, assumptions, and simplifications that were made throughout the research. Thus, to prove that the method was found to be the most suited metric in measuring sector complexity, a more extensive research regarding its performance and robustness should be done in the future. More comprehensive research on sector complexity has to be done in order to have a better understanding of sector complexity and controller workload. Also, to keep up with the relevance of the current situation, the extension of the SSD to the third dimension is crucial.
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