Existing simulation methods and tools available for air traffic complexity research have several limitations (such as time-consuming processing and complex data extraction) that hinder the simple and flexible collection of input data acquired from air traffic controllers (ATCOs). These limitations are particularly evident in research when representative and diverse data are needed, such as research on air traffic complexity based on ATCO input. To address this research gap, we present a new methodological framework for research in air traffic complexity, which incorporates ATCO input. The proposed methodological framework consists of three major components: (1) SATSI, a user-friendly interface for creating and visualizing various static air traffic situations (airspace, traffic, and contextual data), (2) a parser that converts SATSI outputs into inputs for the trajectory prediction model, and (3) an algorithm for automated extraction of terminal air traffic complexity indicators. All together, these components present a novel flexible tool for traffic scenario development, and its integration with the existing trajectory model and automatic processing of air traffic complexity data extraction. The proposed integrated framework shortens the overall research process by using simple and flexible air traffic scenario generation, facilitates automated data collection and enables broader and more representative studies of ATCO-perceived complexity.

Air traffic complexity is the degree of difficulty in monitoring and managing a specific air traffic situation, and it is one of the main drivers of Air Traffic Controller (ATCO) workload. Previously developed terminal air traffic complexity models and methods require significant adjustment or additional data to be applied to unseen airspaces, or to be compared with ATCOs’ complexity perception. To address this gap, a terminal air traffic complexity model based on approach ATCO tasks was developed. Building on previous work, approach ATCO tasks were quantified and used as input features. A new dataset consisting of three different terminal airspaces, each containing a different number of situations, was created. Certified approach ATCOs evaluated the complexity of each situation on a scale of 1 to 5, and these values were used as ground truth for training ordinal regression models. One linear and one non-linear model were trained and evaluated, and both were tested on an airspace unseen during training. The two models achieved strong performance on the tested airspace with Quadratic Weighted Kappa (QWK) values of 0.814 and 0.873, confirming that the proposed task features can predict the complexity of a new, unseen airspace.