Empirical analysis of movement characteristics and collision risks in pedestrian–e-bike flows at signalised crosswalks

The rapid increase in e-bike use has intensified interactions with pedestrians at signalised crosswalks, creating new safety challenges. Existing work often relies on simplified settings or small-scale observations, leaving a gap in understanding how micro-behavioural patterns such as avoidance strategies and spacing translate into measurable collision risks. This study addresses that gap through high-resolution analysis of real-world mixed flows. High-resolution drone footage was collected from a busy signalised intersection in Wuhan, China, to capture naturalistic mixed pedestrian–e-bike flows. From 110 pedestrian and 97 e-bike trajectories, quantitative indicators including instantaneous speed, acceleration, steering angle, nearest-neighbour relative distance, and time-to-collision (TTC) were computed to assess movement dynamics, avoidance behaviour, and collision risk. Results suggest E-bike riders maintained higher and more stable speeds with smaller steering adjustments, whereas pedestrians moved more slowly but relied on frequent accelerations and directional changes to avoid conflicts. Pedestrians preserved smaller and more consistent safety buffers, while e-bike riders required larger and more variable spacing. Time-to-collision analysis revealed that pedestrians faced substantially higher collision risks, particularly in pedestrian–e-bike encounters. Our findings highlight the asymmetric nature of risk in pedestrian–e-bike interactions and provide actionable evidence for crosswalk and signal design. The results can inform safety guidelines and infrastructure strategies such as lane separation, adaptive signal timing, and public education to reduce unintentional injuries and improve safety for vulnerable road users in mixed urban environments.