Exploring pedestrian gap acceptance behaviour using immersive CAVE experiments: A multilevel logit regression model

Unsafe crossing behaviour is a key contributory factor to pedestrian injuries. Understanding the influences of potential factors on pedestrian crossing behaviour is essential. Previous studies have examined the relationship between pedestrian behaviour, road environments, and traffic characteristics. However, the influences of psychological factors, such as safety perception, on pedestrian decision-making are rarely considered. This study investigates the influences of environmental factors, vehicle attributes, personal demographics, and safety perceptions on the gap acceptance behaviour of pedestrians at mid-block crossings using a hybrid experiment and attitudinal survey approach. For instance, a Cave Automatic Virtual Environment (CAVE) method is employed to enhance the immersive experiences of 3-dimensional road environments and dynamic traffic characteristics for pedestrians. A multilevel logit regression method is then employed, controlling for the interdependency between multilevel factors: (1) Participant level: demographics, safety attitude; (2) Trial level: road environment, traffic control, vehicle mix; and (3) Observation level: vehicle class, gap size, and waiting time, in the association measure. Results indicate that the likelihood of gap acceptance increases with pedestrian age, risk-taking attitude, speed limit, gap size, and waiting time. In contrast, the likelihood of gap acceptance decreases with the increased perceived control, presence of on-street parking, and presence of heavy vehicles. These findings shed light on effective remedial measures, such as targeted road safety education and local area traffic management, that can mitigate pedestrian crash risk at high-risk locations with frequent pedestrian-vehicle interactions. Therefore, overall pedestrian safety can be improved, and walkability can be enhanced in the long run.