Roundabouts are a common traffic infrastructure, which are supposed to facilitate safe and smooth traffic flow. Electric bikes as a common traffic tool in a lot of cities in China play an important role in relieving traffic congestion due to the rapid increase of motor vehicles on roads. However, compared with cyclists, e-bikers are more vulnerable because of their higher speed when colliding with motor vehicles. In this research, the spatial-temporal distribution characteristics of conflicts between motor vehicles and electric bikes (e-bikes) at signalized roundabouts are explored. First, the time distance method was used to identify conflicts, and the time to collision (TTC) was selected as the discrimination index we proposed for two representative conflict types. On conflict heat maps and conflict distribution during one signal cycle, we found a series of spatial-temporal conflict distribution regularities. Spatially, the proposed two representative types of conflicts were mainly distributed at exit areas and near the outermost circulatory lanes. Conflict Type 1 was mainly distributed around the outermost motor vehicle lanes, and Type 2 was mainly distributed inside the outermost vehicle lanes and behind the second stop line. In time span, both types of conflicts showed rapid increase before reaching a peak at 30 percentile green time and then decreasing gradually after that peak. Type 1 presented a sharp increasing range during 0–10 percentile green time, while the sharp increasing range for Type 2 presented during the 10–20 percentile. The conclusions developed by this article could provide a theoretical basis for improving traffic safety at roundabouts.

Right-turning vehicles at intersections permitting right turn on red (RTOR) frequently conflict with pedestrians, posing significant safety risks. Existing studies often simplify vehicle trajectories by treating vehicles as centroid points, ignoring the spatial disparities between pedestrians and vehicles. To address this gap, we propose a conflict risk assessment framework based on front and rear wheel trajectories (FRWTs), which accounts for the dynamic differences in vehicle segments during turns. First, we partition vehicles into four segments (inner/outer and front/rear wheels) and develop a trajectory prediction model to quantify risk variations across these segments. Our analysis reveals that the inner front wheel poses the highest collision risk due to its speed, trajectory curvature, and pedestrian proximity. Next, we introduce three conflict interaction modes—hard interaction, no interaction, and soft interaction—and evaluate the applicability of conflict indicators (e.g., Time to Collision (TTC) and Post-Encroachment Time (PET)) under each mode. Using a Support Vector Machine (SVM) classification algorithm, we classify risk severity with high accuracy: 96% for hard interaction, 96% for no interaction, and 97% for soft interaction modes when TTC-PET dual indicators are employed. Our findings demonstrate that FRWT-based modeling significantly improves conflict risk assessment accuracy compared to centroid-point approaches. This work provides actionable insights for proactive traffic safety management and supports the development of targeted conflict mitigation strategies at RTOR intersections.