Modeling Interactions between Pedestrians and Right-Turn Vehicles at Signalized Intersections
Bibliographic record
Abstract
Aside from accidents caused by pedestrian signal violation, crashes between pedestrians and turning vehicles also happen frequently. At most signalized intersections in China, pedestrians are released together with the parallel right-turn vehicles, which results in frequent conflicts between the pedestrians and the turning vehicles. An empirical study has been carried out at three typical crosswalks at signalized intersections in Shanghai. It has been found pedestrians yield twice as often as right-turning vehicles. Based on a Pearson correlation analysis, the key factors of pedestrian yielding behavior are identified, which includes the existence of a pedestrian group, group population, crossing direction, disturbance of non-motorized vehicles, the volume of right-turn vehicles, the distance to the conflict point of pedestrians, and the time difference between pedestrians and right-turn vehicles arriving at the conflict point. A binary logistic regression model has been developed for the pedestrian's yielding decision, followed by an elasticity analysis of influencing factors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".