Pedestrians’ unsafe road-crossing behaviors in Iran: An observational-based study in West Azerbaijan
Bibliographic record
Abstract
Pedestrians are one of the most vulnerable users in road traffic injuries (RTIs). The rate of pedestrians’ fatality is high in Iran. It is worthwhile to investigate how pedestrians behave. This observational study aimed to investigate pedestrians’ unsafe behaviors while crossing. This cross-sectional study examined the behavior of 1095 pedestrians (69.7% men) using videotaping when they crossed at two intersections and three non-intersections on a weekend and two working days in the morning, at noon, and in the evening. The information obtained was classified into 5 domains including adherence to traffic rule, violation, environmental barriers, visibility, and distraction. Data were analyzed using Stata version 17. About 60% of the pedestrians ignored the crosswalk and crossed the street wherever they wanted. More than 30% ignored the vehicles passing and crossed the street inattentively. About 60% of the pedestrians committed violations. More than half of pedestrians crossed unsafe crossings diagonally or in a hurry. More than 35% wore dark clothing and had low visibility, and nearly 30% were distracted. Adolescent pedestrians did not adhere traffic rules about 6 times more than the young adult pedestrians. Pedestrians who did not adhere to traffic rules in the morning were significantly more than in the evening. Men committed a violation 1.47 times more than women. The results showed that the pedestrians committed a violation in the morning significantly more than in the evening. The occurrence of pedestrians’ unsafe behaviors in Maku was high. Unsafe behaviors were high among men and young adult pedestrians. Therefore, it’s essential to implement educational interventions via different media as well as environmental interventions by different organizations to improve safe behavior among pedestrians.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".