Learning to Explain Air Traffic Situation

TL;DR

This paper introduces a Transformer-based multi-agent trajectory model that explains complex air traffic situations by quantifying aircraft influence, aiding air traffic controllers' understanding and decision-making.

Contribution

It presents a novel machine learning framework that captures comprehensive air traffic dynamics and provides explainable insights using attention scores from a Transformer model.

Findings

Effective explanation of air traffic situations demonstrated on real-world data.

Model captures both movement and social interactions of aircraft.

Potential to improve air traffic controllers' situational awareness.

Abstract

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.