Mental workload in air traffic control: an index constructed from field tests

Area of Science:

• Aviation Psychology
• Human Factors Engineering
• Cognitive Science

Background:

• Mental workload assessment is critical in air traffic control (ATC), but traditional methods using only objective (e.g., aircraft numbers) or subjective (e.g., stress levels) factors yield inconsistent results.
• Integrating dynamic air traffic data with fluctuating human psychological variables presents a significant challenge in workload evaluation.

Purpose of the Study:

• To develop and validate a novel method for estimating mental workload in ATC that combines objective traffic load with subjective user-perceived load.
• To create a Traffic Load Index (TLI) capable of identifying periods of increased mental demand and quantifying the associated workload.

Main Methods:

• A Traffic Load Index (TLI) was developed using 25 hours of radar control recordings from 25 air traffic controllers.
• The TLI was designed to account for objective traffic variables and subjective factors like conflict seriousness and time pressure.
• The TLI's performance was compared against a simple aircraft count and subjective workload ratings (NASA-TLX).

Main Results:

• The TLI demonstrated a stronger correlation with self-rated workload across the entire group of controllers compared to using aircraft count alone.
• In individual controller analyses (intrasubject), the TLI showed a better correlation with workload ratings in 77.8% of cases compared to the simple aircraft count.
• The TLI effectively identified time boundaries associated with increased mental load.

Conclusions:

• Effective mental workload estimation in ATC necessitates the integration of both objective task-related variables and subjective evaluations.
• The developed Traffic Load Index (TLI) offers a promising approach for more accurate and comprehensive workload assessment in air traffic control.