An Analysis of the Influential Factors of Violations in Urban-Rural Passenger Transport Drivers
Bibliographic record
Abstract
Road passenger transport is important for keeping urban and rural areas connected. In order to explore the traffic safety mechanisms behind urban and rural passenger transport, the personal attributes of urban-rural bus drivers from different areas were investigated. Based on the binary logistic regression model, an impact analysis model of 14 indicators of bus driver violations was established. The results showed that personality, gender, bus route, road conditions, and nap habits were important factors that affect driver violations. Female drivers violated slightly more (27.7%) than male drivers (26.2%), but male drivers violated multiple times (2.1), which was significantly higher than female drivers (1.5). Drivers with choleric personality were more likely to violate the traffic rules than others. Rural bus drivers violated significantly more (32.7%) than urban bus drivers (8%). The violation proportion of drivers who usually take naps but were deprived of naps (35.3%) was higher than that of drivers who have no nap habits (21.8%). The research results can act as a reference for improving urban-rural traffic safety.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".