Machine Learning Analysis of Pedestrians’ Hazard Anticipation from Eye Tracking Data

Pedestrian tourists are considered the most vulnerable road users of urban mobility environments. Tourists are a special category of pedestrians, exhibiting different visual behaviour to residents due to their enthusiasm and unfamiliarity with the environment. These characteristics of pedestrian tourists influence their hazard perception. Eye tracking technology became popular in investigating pedestrian safety problems after findings that eye-gaze behaviour is linked with human attention and hazard anticipation. The majority of eye-tracking studies to date use stationary technology that may miss out important properties relating to environmental dynamics that cannot be accurately simulated. This study employs a novel method utilising mobile eye-tracking technology in naturalistic settings to investigate the application of machine learning in identifying differences between tourist and resident pedestrians’ visual behaviour. Eye tracking metrics are used to train an Extreme Gradient Boost (XGBoost) model to examine whether tourists have less hazard perception than residents when visiting destinations with opposite driving conventions to their own. Preliminary results with a small group of tourist and resident pedestrians demonstrate how such machine learning models could be used in real-time by agent-based systems that utilise wearable augmented reality displays to support hazard perception of tourist pedestrians.