Decoding the influencing factors of pedestrian-automated vehicle interaction in shared spaces: A multi-level path analysis approach

In the near future, pedestrians will increasingly face automated vehicles (AVs) in urban environments, particularly in shared spaces. Ensuring safe and effective pedestrian-AV interactions requires a deeper understanding of the factors that affect pedestrian behaviour. This study examines how environmental and individual factors influence pedestrians' perception and crossing behaviour in front of AVs in shared spaces. A virtual reality (VR) experiment with 60 participants was conducted to simulate diverse traffic scenarios, and both subjective and behavioural data were collected after each trial. Using multi-level path analysis, we modelled the direct and indirect effects of environmental factors (e.g., lane width, visual load, surface condition, time of day, traffic markings, traffic conditions) and individual factors (e.g., age, gender, educational level, personality) on perceived safety, comfort, legibility, trust and behavioural outcomes including crossing initial time, crossing duration and safety margin. The findings highlight that traffic markings and traffic conditions are the most influential factors, while educational level, transport modes, and personality traits also play a significant role. For example, the presence of zebra and yielding AV behaviours were associated with more positive perception and safer crossing behaviour. Participants with higher education levels and greater openness tended to show more supportive attitudes during interactions with AVs. In addition, perception served not only as an outcome but also as a mediator associating context and behaviour. The results provide valuable insights for enhancing the design of AV systems and shared spaces to improve pedestrian safety and trust.