10 - Safety and Security
UNCERTAINTY ANALYSIS AND EYE-TRACKING-BASED CLUSTERING IN AIR TRAFFIC CONTROLLER WORKLOAD PREDICTION
A. Lemetti¹, L. Meyer², M. Peukert², T. Polishchuk¹, C. Schmidt¹, H. Alpfjord Wylde²; ¹Linköping University (LiU), Sweden ;²Air Navigation Services of Sweden (LFV), Research & Innovation, Sweden
We analyze how socio-demographic, temporal, and variability-based factors shape machine-learning workload prediction from eye-tracking and head-movement data of air traffic controllers. Time-on-task raises performance slightly, while experience and clustering based on variability in eye-tracking and head-movement patterns yield larger gains, showing that models benefit when they incorporate individual differences.
DOI: 10.71945/icas2026_0118