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Team workload is a team-level construct considered similar to, but not reducible to, individual workload and mediated by team coordination. Despite this, the conceptualization and measurement of team workload in action teams lags behind that of individual workload. In most empirical studies, team workload is often simply considered as the sum or average of individual team members’ workload. However, unique characteristics of action teams, such as interdependence and heterogeneity, suggest that traditional approaches to conceptualizing and measuring team workload may be inadequate or even misleading. As such, innovative approaches are required to accurately capture this complex construct. This paper presents the development of a simulation designed to investigate the influence of interdependence and demand levels on team workload measures within a 3-person action-team command and control scenario. Preliminary results, which suggest that our manipulations are effective, are provided and discussed. 

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 Link. 

The present research examines a pattern-based measure of communications based on Closed Loop Communications (CLC) and non-content verbal metrics to predict Loss of Separation (LOS) in the National Airspace System (NAS). This study analyzes the transcripts from six retired Air Traffic Controllers (ATC) who participated in three simulated trials of various workloads in a TRACON arrival radar simulation. Results indicated a statistically significant model for predicting LOS based on CLC deviations (CLCD), word count in transmission, words per second, and traffic density. However, more research is required to evaluate the significance of each communication variable to predict LOS.