Extraction and Investigation of Dominant Eye-Gaze Pattern in Train Driver’s Visual Behavior Using Markov Cluster Algorithm

In the present study, dominant eye-gaze patterns in professional train drivers' visual behavior are investigated using the Markov Cluster (MCL) algorithm. Applying the MCL algorithm results in a common gaze pattern showing a sort of perception tactic the drivers usually follow. The drivers repetitively move their gaze ahead soon after looking at somewhere else, independently of their years of experience. They are, however, found different in that experienced drivers can consistently follow the tactic while inexperienced drivers cannot. Time variation in the number of attentive pattern deviations demonstrates that, as well as the inexperienced drivers made higher frequency and larger fluctuations of pattern deviation, there were several particular segments in the route in which intensive pattern deviations arose in common. Inexperienced drivers would make intensive pattern deviations in such route segments that may have higher requirements of their cognitive resources.