Modeling Pedestrian Temporal Violations at Signalized Crosswalks: A Random Intercept Parametric Survival Model
Bibliographic record
Abstract
This study aims to model pedestrian temporal violation behavior at signalized crosswalks. Video data of pedestrian crossing behavior were collected from three locations in China and were used to investigate the effect of several factors on pedestrian temporal violation behavior. The temporal violation behavior was analyzed using the relationship between pedestrian waiting duration and their endurance probabilities. A fully parametric duration model with Weibull distribution was used to model the temporal violation behavior, and the cluster-specific heterogeneity among the three study sites was accounted for using random intercepts. Six variables were identified to significantly affect the violation behavior: pedestrian gender and phone distraction status, location type, pedestrian volume, day of the week, and time of the day. The results show that pedestrians are likely to disobey traffic regulations when there are longer waiting durations. Male pedestrians have a higher violation tendency than females. Pedestrians distracted by their phones have longer waiting durations than undistracted pedestrians. Signalized road segment crosswalks are associated with higher temporal violation propensity than signalized intersection crosswalks. Pedestrians are more likely to commit violations at higher pedestrian densities. Weekdays are associated with shorter waiting durations and higher violation tendency than weekends. Pedestrians are more likely to violate traffic regulations in the morning than at midday and in the evening. These findings give insights into the pedestrian crossing behavior to better accommodate pedestrians and improve safety.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".