Learning to Explain Air Traffic Situation
This addresses the challenge of enhancing decision-making and situational awareness for air traffic controllers, though it appears incremental as it builds on existing machine learning methods applied to a specific domain.
The paper tackles the problem of understanding how air traffic controllers mentally model complex traffic situations by proposing a Transformer-based multi-agent trajectory model that quantifies aircraft influence using attention scores, trained on real-world data from Incheon International Airport to provide explainable insights.
Understanding how air traffic controllers construct a mental 'picture' of complex air traffic situations is crucial but remains a challenge due to the inherently intricate, high-dimensional interactions between aircraft, pilots, and controllers. Previous work on modeling the strategies of air traffic controllers and their mental image of traffic situations often centers on specific air traffic control tasks or pairwise interactions between aircraft, neglecting to capture the comprehensive dynamics of an air traffic situation. To address this issue, we propose a machine learning-based framework for explaining air traffic situations. Specifically, we employ a Transformer-based multi-agent trajectory model that encapsulates both the spatio-temporal movement of aircraft and social interaction between them. By deriving attention scores from the model, we can quantify the influence of individual aircraft on overall traffic dynamics. This provides explainable insights into how air traffic controllers perceive and understand the traffic situation. Trained on real-world air traffic surveillance data collected from the terminal airspace around Incheon International Airport in South Korea, our framework effectively explicates air traffic situations. This could potentially support and enhance the decision-making and situational awareness of air traffic controllers.