Methodological Framework for Generation of Static Air Traffic Situations and Automated Complexity Data Extraction

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.