Safety evaluation of urban roundabouts under heterogeneous traffic: a framework integrating drone-based traffic conflict analysis, predictive modeling, and sensitivity analysis

This study evaluates the safety performance of urban roundabouts in non-lane-based, heterogeneous traffic environments typical of low- and middle-income countries, focusing on identifying vulnerable locations within roundabout areas, classifying conflict types across different zones, and providing reproducible, easy-to-interpret visualizations for practitioners and stakeholders. High-resolution drone-recorded data from 23 Indian roundabouts were processed for trajectory extraction. Traffic conflicts were detected using time-to-collision (TTC) (≤ 1.5 seconds) and post-encroachment time (PET) (≤2.5 seconds) and classified (rear-end, lane-changing, crossing) according to conflicting angles. A conflict frequency prediction model was developed using the XGBoost (extreme gradient boosting) algorithm. Sensitivity analysis was performed using the Shannon Entropy-based method alongside SHapley Additive exPlanations (SHAP) to rank the influencing factors. Results showed significant clustering of critical conflicts (low TTC and PET) in weaving zones, entries, and exits. Spatial heatmaps and conflict-maps pinpointed high-risk hotspots and illustrated the distribution of conflict types. The conflict frequency prediction model demonstrated high predictive accuracy (root mean squared error (RMSE) = 178.15, mean absolute error (MAE) = 139.42, R 2 = 0.85). Sensitivity analysis identified circulating and approaching traffic volumes, average speed, pedestrian volume, traffic composition, occupancy time, circulatory roadway width, inscribed circle diameter, entry, exit, and weaving dimensions as key factors influencing conflict frequency. The integrated framework provides data-driven guidance for targeted infrastructural improvements and traffic management strategies for roundabout safety enhancement in heterogeneous settings.