Air traffic control (ATC) is a safety-critical service system that demands
constant attention from ground air traffic controllers (ATCos) to maintain
daily aviation operations. The workload of the ATCos can have negative effects
on operational safety and airspace usage. To avoid overloading and ensure an
acceptable workload level for the ATCos, it is important to predict the ATCos'
workload accurately for mitigation actions. In this paper, we first perform a
review 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 labels
are obtained. The simulations are conducted under three Phoenix approach
scenarios while the human ATCos are requested to self-evaluate their workload
ratings (i.e., low-1 to high-7). Preliminary data analysis is conducted. Next,
we propose a graph-based deep-learning framework with conformal prediction to
identify the ATCo workload levels. The number of aircraft under the
controller's control varies both spatially and temporally, resulting in
dynamically evolving graphs. The experiment results suggest that (a) besides
the traffic density feature, the traffic conflict feature contributes to the
workload prediction capabilities (i.e., minimum horizontal/vertical separation
distance); (b) directly learning from the spatiotemporal graph layout of
airspace with graph neural network can achieve higher prediction accuracy,
compare to hand-crafted traffic complexity features; (c) conformal prediction
is a valuable tool to further boost model prediction accuracy, resulting a
range 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/}{Link}