Analysis of Train Driver's Eye-Gaze Movement Patterns Using Markov Cluster Algorithm

Abstract

The present paper investigates train drivers' eye-gaze movements to find characteristic attentive patterns that can distinguish different levels of driver expertise. Eye-gaze data were collected from early-morning and nighttime observation studies in which six active train drivers participated. The acquired eye-gaze data were processed with Markov Cluster Algorithm which generates composites of Area-of-Interest (AoI) clusters, whose structure characterizes individual drivers' attentive behavior. Resultant structures are compared with one another to specify characteristic transition patterns that have a small number of hubs with different strengths within AoI networks according to the driver's level of expertise and the driving situation.