Analysis of Train Driver's Visual Perceptual Skills Using Markov Cluster Algorithm

Abstract

The present paper investigates eye-gaze data of professional train drivers with different years of experience using Markov Cluster Algorithm (MCL) in order to extract characteristic eye-gaze patterns fostering a better understanding of their visual perceptual skills. MCL distilled a basic eye-gaze pattern in their visual behavior indicating that all the drivers would repetitively move their gaze ahead soon after looking at another area of interest, but they were found different in the "strength" of the pattern depending on their level of expertise. It was also clarified that inexperienced drivers made frequent deviations from the basic eye-gaze pattern in particular segments of the route where they had to deal with multiple tasks in parallel imposing higher cognitive loads of them.