Conflict Risk Assessment Between Pedestrians and Right-Turn Vehicles: A Trajectory-Based Analysis of Front and Rear Wheel Dynamics
Why this work is in the frame
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Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it