A study of the cognitive process of pedestrian avoidance behavior based on synchronous EEG and eye movement detection

Abstract

Pedestrian avoidance behavior often occurs in underground public spaces that connect urban rail transit and commercial complexes. This study proposes a co-monitoring method based on eye movement and electroencephalogram (EEG) to study pedestrian avoidance behavior in a real environment, taking the underground public space of the commercial complex of the Luoxiong Road railway station in Wuhan City as an experimental site. It is found that pedestrian avoidance behavior is influenced by both personal and environmental factors. The pedestrian avoidance behavior is a comprehensive response to the evaded person and the current environment. The personal factors mainly affect the pedestrian avoidance mode, while the environmental factors mainly affect the frequency of avoidance behavior. Avoidance patterns are related to the tendency of Chinese pedestrians to walk right, and the frequency of avoidance behavior is related to the complexity of the intersection of pedestrian walking routes within the environment, so avoidance behavior can be reduced by using spaces with good spatial connectivity in the design of underground public spaces. These findings provide theoretical support and data supplement for future environmental design optimization of underground public spaces.