Study on Road-Crossing Violations among Young Pedestrians Based on the Theory of Planned Behavior
Bibliographic record
Abstract
Young pedestrians have a high rate of traffic violations and are vulnerable. In this study, theory of planned behavior (TPB) questionnaires were collected from a sample of 395 young pedestrians. Reliability analysis demonstrated that the TPB questionnaire was effective and credible. An analysis model was established based on the TPB. The motivations for traffic violation behaviors among young pedestrians on intersections were studied from the perspective of social psychology. The results revealed that the most common violation behavior of young pedestrians was crossing on yellow light (29.7%). Male young pedestrians reported the higher intention of violating regulations. Behavioral attitude (0.14), subjective norm (0.17), and perceived behavioral control (0.12) all affected young pedestrians’ behavioral intentions. Relatives and friends played a positive role in mitigating young pedestrians’ intentions to commit violations at intersections. Perceived behavior control had the weakest influence on young pedestrians’ intentions to violate regulations. Behavioral intention (0.31) was the most direct and significant predictor of behavior. The results of the study are valuable for the identification of the causes of traffic violations among young pedestrians, and they can serve as a reference for the implementation of effective interventions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".