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

Air Traffic Controller (ATCo) Mental Workload (MW) is likely to remain the single greatest functional limitation on the capacity of the ATM (Air Traffic Management) System. There is a need to develop computational models for monitoring real-time MW to facilitate the development of different approaches for task support. MW in the ATM domain has been attempted to be estimated and monitored using subjective, physiological and behavioural measures. However, the literature highlighted disadvantages with current subjective and physiological methodologies used to assess MW related to their impracticality in real work-environments. Therefore, what is needed is an unobtrusive MW calculation model based on ATCos' recordable behaviours that can be deployed unobtrusively in an ecologically valid environment. This research aims to offer a first-approach model of ATCos' MW based on data that can be collected from the log of the technological systems used for ATM: 1) their communication patterns and 2) their actions with the ATM technical systems. In a next stage, this model will be further validated during simulation sessions with real ATCos, using physiological measures (eye-tracking), alongside subjective measures of MW. The main outcome of this research project will be a real-time MW non-intrusive and automatic monitoring tool that would allow ATCOs and ATM systems to get adapted to task complexity variations throughout time, mitigating the disastrous effect of drops in human performance.

Keywords: Air traffic management, Mental workload, Human performance, Risk management, Computational model.

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