The attentional guidance and facilitating effects of group behavioral cues on individual college pedestrians' jaywalking decisions.

OBJECTIVE: Jaywalking is an important cause of pedestrian-related automobile accidents. Exploring the factors that influence jaywalking behavior and suggesting appropriate improvement measures are critical for reducing automobile accidents involving pedestrians.

METHODS: This study divided traffic situations into high-risk and low-risk situations. Each situation contained three visual attention cues: vehicle, traffic light, and group behavior. Based on this, the role of visual cues in guiding pedestrians' attention and influencing their decisions during jaywalking was examined. Sixty participants, with an average age of 19, were recruited. They were shown 84 crosswalk videos randomly while their crossing decisions and eye movement data were recorded.

RESULTS: In low-risk situations, pedestrians spent more attention on group behavioral cues when making jaywalking decisions. The rate of jaywalking increased with the number of other jaywalking pedestrians. In high-risk situations, the pedestrians' total fixation duration at vehicle hazard cues was longer when making jaywalking decisions, and the jaywalking rate decreased.

CONCLUSIONS: The results indicate that pedestrians' jaywalking decisions were based on other pedestrians' illegal crossing cues and automatic associative processes in low-risk situations. The higher the number of people crossing the street, the higher the number of pedestrians illegally crossing the road. In high-risk situations, pedestrians paid more attention to vehicle hazard cues before making jaywalking decisions, and fewer illegal crossings. The jaywalking decisions were based on a risk assessment, a controlled analytical process. The results verify the effect of visual cues on pedestrians' attentional guidance and decision-making in different traffic situations, as well as the effectiveness of visual attention in predicting decision intention. The findings provide a theoretical basis and data reference for pedestrian safety education and constructing an intelligent driving pedestrian trajectory prediction model.