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Air traffic control (ATC) is a safety-critical service system that demandsconstant attention from ground air traffic controllers (ATCos) to maintaindaily aviation operations. The workload of the ATCos can have negative effectson operational safety and airspace usage. To avoid overloading and ensure anacceptable workload level for the ATCos, it is important to predict the ATCos'workload accurately for mitigation actions. In this paper, we first perform areview of research on ATCo workload, mostly from the air traffic perspective.Then, we briefly introduce the setup of the human-in-the-loop (HITL)simulations with retired ATCos, where the air traffic data and workload labelsare obtained. The simulations are conducted under three Phoenix approachscenarios while the human ATCos are requested to self-evaluate their workloadratings (i.e., low-1 to high-7). Preliminary data analysis is conducted. Next,we propose a graph-based deep-learning framework with conformal prediction toidentify the ATCo workload levels. The number of aircraft under thecontroller's control varies both spatially and temporally, resulting indynamically evolving graphs. The experiment results suggest that (a) besidesthe traffic density feature, the traffic conflict feature contributes to theworkload prediction capabilities (i.e., minimum horizontal/vertical separationdistance); (b) directly learning from the spatiotemporal graph layout ofairspace with graph neural network can achieve higher prediction accuracy,compare to hand-crafted traffic complexity features; (c) conformal predictionis a valuable tool to further boost model prediction accuracy, resulting arange of predicted workload labels. The code used is available at\href{https://github.com/ymlasu/para-atm-collection/blob/master/air-traffic-prediction/ATC-Workload-Prediction/}{$\mathsf{Link}$}.

Air Traffic Controller Workload Level Prediction using Conformalized Dynamical Graph Learning