Investigation of Contributing Factors to Traffic Crashes and Violations: A Random Parameter Multinomial Logit Approach
Bibliographic record
Abstract
This study investigates contributing factors to traffic violations by seriousness. The traffic violations are divided into four categories by seriousness (unintentional violation, minor violation, serious violation, and crash with violation). The results of the random parameter multinomial logit model indicate that various factors potentially affect the severity of traffic violations. The key findings include the following: (1) female drivers are more likely to commit minor violations; (2) drivers from an area with a longer travel time to work and a higher proportion of driving to work are more likely to have minor violations and serious violations, while those from the high-income area are less likely; (3) drivers are more likely to be associated with a more minor infraction during the afternoon peak (4 p.m.–6 p.m.). The results from this study are expected to be beneficial for policymakers and traffic police to comprehend the factors affecting violations and implement effective strategies to minimize the number and seriousness of traffic violations.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".