Using Eye Movement Data Visualization to Enhance Training of Air Traffic Controllers: A Dynamic Network Approach.

Area of Science:

• Human-Computer Interaction
• Cognitive Science
• Aerospace Engineering

Background:

• The Federal Aviation Administration (FAA) predicts increased US air traffic, necessitating more Air Traffic Control Specialists (ATCSs).
• Current ATCS training may be enhanced by analyzing expert eye movement (EM) characteristics, but visualizing EM for dynamic tasks is challenging.
• Dynamic displays with multiple, moving, and overlapping targets complicate effective EM analysis.

Purpose of the Study:

• To introduce a novel dynamic network-based approach for in-depth eye movement (EM) analysis in air traffic control.
• To integrate adapted visualizations and network science measures for interpreting complex visual attention patterns.
• To explore the utility of this approach in improving ATCS training and performance.

Main Methods:

• Developed a dynamic network-based approach integrating time-frame networks and normalized dot/bar plots.
• Applied network science measures (indegree, closeness, betweenness) for EM analysis.
• Utilized a high-fidelity simulator with veteran ATCSs and pseudo-pilots for an aircraft conflict task.

Main Results:

• The approach effectively interprets and supports the analysis of ATCS visual attention to dynamic, multi-element targets.
• Multiple lines of evidence from visualizations and network measures provide robust insights into EM.
• Eye fixation duration or count alone does not always correlate with a target's importance in the visual attention flow.

Conclusions:

• The dynamic network-based approach offers a promising method for cohesively analyzing and visualizing EM characteristics.
• This methodology can significantly enhance the effectiveness of ATCS training programs.
• Improved understanding of visual attention can lead to better performance in complex air traffic control scenarios.