Contents Introduction
Air traffic is predicted to grow worldwide in the coming decades. In many enroute regions air traffic is expected to exceed current capacity limits, i.e. the maximum number of aircraft allowed in a given airspace, as defined by controllers. To accommodate high levels of throughput, while maintaining safety, semi-automated and fully-automated conflict resolution algorithms will be required as a support tool for the air traffic controllers [1]. One of the principle goals of the research in defining complexity maps is to objectively and accurately determine the capacity of a given element of an airspace (sector), since there are significant costs associated with miscalculating airspace capacity: an underestimated capacity leads to underutilized airspace and unnecessary holds and rerountings, whereas an overestimated capacity may lead to congestion delays or safety breaches with respect to minimum aircraft separation. This study is a first step aimed at determining the complexity of an airspace under automated conflict resolution control. In this study, we consider then with the airspace as a “closed-loop” system, where the automated conflict resolution control can be seen as the feedback loop. This method should allow air traffic managers/controllers to predict in real-time airspace complexity for a given traffic configuration (routes and flow rates characteristics), and then could be considered as an easy-to-use airspace health prediction tool for the air traffic managers.
