Assessing the Cognitive and Operational Impacts of AI in Airspace Configuration for Flow Management Positions

Abstract

As European airspace approaches its operational saturation limits, the Air Traffic Management community is increasingly turning to artificial intelligence to support the complex task of Demand and Capacity Balancing. This paper presents preliminary findings from a human-in-the-loop Real-Time Simulation testing an AI-based tool for Dynamic Airspace Configuration, to increase the flexibility and dynamicity of airspace management to better accommodate growing traffic demands during baseline, nominal and contingency situations. Preliminary results show the possible impact of the introduction of an AI-based decision-support tool in operating procedures for airspace configuration, especially on Flow Management Position operators’ cognitive workload, situation awareness, trust. The discussions with the front-end operators also highlighted how the AI decision-support tool could have broader implications for the future skills, competencies, and responsibilities of the Flow Management Position (FMP). Specifically, the need for targeted training programs as well as organizational and regulatory discussions to define which traditional skills must be preserved and which new supervisory competencies must be acquired to maintain FMPs’ accountability, and adequate human oversight.