The human as a resource in future Unmanned Aerial Traffic systems

The growing complexity of urban airspace due to increasing drone operations requires novel approaches to traffic management and coordination. This thesis investigates the role of the air traffic controller in future drone coordination with a focus on cognitive demands during conflict management in urban drone traffic. The study aims to identify the tasks performed by controllers, the cognitive processes involved, and how future Unmanned Traffic Management (UTM) systems can support human decision-making in such scenarios. A qualitative and abductive research approach was applied. Data were collected through semi-structured group interviews with experienced air traffic controllers using scenarios in the UTM City simulator. The data were analyzed through thematic analysis and further examined using Cognitive Task Analysis (CTA) and the Joint Control Framework (JCF). These methods enabled an in-depth mapping of tasks and cognitive levels activated during conflict resolution. The findings highlight several recurring challenges for drone coordinators, including maintaining situational awareness, prioritizing between operations, and managing conflicting airspace demands. The analysis identified high cognitive load particularly at the “implementation” and “frame” levels of the JCF model. The results also suggest that future UTM systems should provide timely information, clear visualizations of airspace conflicts, and adaptive support for decision-making to avoid cognitive overload and Automation Surprise. This study contributes to the design of human-centered UTM systems by offering insights into the human role in future drone traffic management.