Reduction of Air Traffic Complexity Using Trajectory-Based Operations and Validation of Novel Complexity Indicators

Airspace capacity is limited primarily by the saturation of air traffic controller's capacity, whose workload increases as air traffic complexity increases. Workload can be reduced through task automation by advanced controller tools. Automation and the development of novel controller tools is therefore one of the key aspects of future concepts of operations in European and American air traffic management systems. Implementation of trajectory-based operations (TBOs) has been proposed as a way to reduce workload, but few studies have examined how TBO affects air traffic complexity. This paper compares air traffic complexity experienced by ten air traffic controllers in a real-time simulation environment involving conventional operations and TBO. Analysis of subjective complexity scores collected in real time showed that TBO significantly reduced complexity when at least 70% of aircraft were flying according to TBO and when the airspace was occupied simultaneously by more than 15 aircraft. Subjective complexity scores were tested for correlation with 20 commonly used complexity indicators, and six indicators were used to generate a predictive linear model that performed well in conventional operations but less well under TBO. Therefore, we defined and experimentally validated two of seven novel TBO-specific complexity indicators. A second correlation model combining these two novel indicators with four already in use generated much better predictions of complexity than the first model.