Developing a First-Approach Model of Air Traffic Controllers' Mental Workload based on Behavioural Measures: A Theory for Modelling Air Traffic Controllers' Mental Workload

Muñoz-de-Escalona, Enrique; Leva, Chiara; Frutos, Patricia Lopez de · 2023 · Crossref

DOI: 10.3850/978-981-18-8071-1_p527-cd

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Summary

This paper addresses the critical challenge of monitoring Air Traffic Controllers’ (ATCos) Mental Workload (MW), which remains the primary functional limitation on Air Traffic Management (ATM) system capacity. With global air traffic projected to double every 15 years, maintaining safety and efficiency requires real-time MW assessment. Existing methods relying on subjective reports or physiological measures are deemed impractical for operational environments due to their obtrusiveness, potential to distort performance, and interference with task execution. Consequently, the authors propose developing a computational model that estimates MW unobtrusively using behavioral data automatically recorded by ATM technical systems, specifically focusing on ATCos’ communication patterns and interactions with technical interfaces. The theoretical framework posits that MW is not solely a function of task complexity but results from the interplay between task demands and the cognitive strategies ATCos employ to manage them. Drawing on the COCOM and SRK human performance models, the paper argues that ATCos modulate MW through metacognition, shifting strategies based on time pressure and situational awareness. Under high workload, controllers may adopt reactive behaviors, such as prioritizing vertical over lateral separation, grouping aircraft monitoring, or relying on heuristic "conflict solving libraries." The study hypothesizes that these internal cognitive states and strategic shifts manifest in observable behavioral changes in voice communications and system interactions, which can be quantified from system logs. The proposed model identifies specific behavioral indicators predicted to correlate with increased MW. Regarding communication form, the authors predict an upward linear trend in communication frequency and duration, an exponential increase in response latency to pilot requests, and a logarithmic increase in speech velocity. They also anticipate shorter control event lengths and greater grouping of control events per communication. In terms of content, higher MW is expected to reduce accessory information, increase the appearance of mistakes and confusion-related phrases, and trigger the use of high-task-demand vocabulary. Additionally, the model considers coordination patterns and specific conflict resolution strategies, such as the prioritization of vertical separation, as indicators of workload modulation. The significance of this research lies in its potential to create a non-intrusive, real-time MW monitoring tool that adapts to task complexity variations, thereby mitigating performance drops and enhancing safety. By leveraging existing ATM automation logs, the model avoids the practical limitations of current assessment methods. The paper outlines this as the first stage of a broader project, with future work involving validation through simulation sessions with real ATCos, utilizing physiological measures like eye-tracking and subjective reports to refine the computational model. This approach aims to shift MW assessment from post-hoc analysis to proactive, real-time support for human performance in high-stakes aviation environments.

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• mental demand
• workload measurement
• capacity resource theory

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• Theoretical Contribution: theory or model