Spatial-Temporal Cluster Approach to Discover Visual Scanning Behaviors in Virtual Reality

If we could discover the visual scanning patterns of expert air traffic control operators (ATCOs), we could use those findings to better train novices. One critical issue is that visual scan paths can be complex even for a short period of time, therefore, a systematic approach is required to obtain clear and meaningful visual scanning patterns. We transformed the raw eye movement data of expert ATCOs into visual scanning patterns by using the collapsed eye movement sequences that occurred on important areas of interest, then visualized them based on accumulated time frames. We collected and subsequently analyzed controller eye movements that occurred before and after controllers issued takeoff clearances, in a high-fidelity virtual reality airport tower. We obtained clear visual scanning patterns from our analyses of eye movement data. We plan additional investigation to determine whether tower controllers can be trained to employ effective visual scanning behaviors