摘要: It is a widely held view that complexity is a key factor that significantly affects the work of an air traffic controller, which, in turn affects capacity. A better understanding of what makes the controllers' work complex will im... 展开 It is a widely held view that complexity is a key factor that significantly affects the work of an air traffic controller, which, in turn affects capacity. A better understanding of what makes the controllers' work complex will improve current and future air traffic management (ATM) capacity, analysis, airspace planning, and future air traffic control (ATC) development. This paper describes our approach to develop a macroscopic model that will give us an automatic and non-subjective method to classify sectors according to their complexity. The first step was to identify the complexity indicators. We combined ATC operational advice with statistical analysis to compile a list of relevant complexity indicators. Clearly, these indicators, their influence and interaction vary amongst sector types. Hence, our next step was to classify our sectors into a small number of homogenous groups, or clusters, to arrive at the sectors' typology. We used two approaches to classify the sectors. The first was based on a K-means classification and the second was by descendant hierarchical clustering - divisive segmentation. Our study shows that our model gave us a meaningful typology and understanding of our sectors' complexity and that we can improve future controller workload and sector capacity predictions at a macroscopic level. 收起