Factors Affecting Crash Involvement of Commercial Vehicle Drivers: Evaluation of Commercial Vehicle Drivers’ Characteristics in South Korea
Bibliographic record
Abstract
The aim of this study was to evaluate the effects of driver-related factors on crash involvement of four different types of commercial vehicles—express buses, local buses, taxis, and trucks—and to compare outcomes across types. Previous studies on commercial vehicle crashes have generally been focused on a single type of commercial vehicle; however, the characteristics of drivers as factors affecting crashes vary widely across types of commercial vehicles as well as across study sites. This underscores the need for comparative analysis between different types of commercial vehicles that operate in similar environments. Toward these ends, we analyzed 627,594 commercial vehicle driver records in South Korea using a mixed logit model able to address unobserved heterogeneity in crash-related data. The estimated outcomes showed that driver-related factors have common effects on crash involvement: greater experience had a positive effect (diminished driver crash involvement), while traffic violations, job change, and previous crash involvement had negative effects. However, the magnitude of the effects and heterogeneity varied across different types of commercial vehicles. The findings support the contention that the safety management policy of commercial drivers needs to be set differently according to the vehicle type. Furthermore, the variables in this study can be used as promising predictors to quantify potential crash involvement of commercial vehicles. Using these variables, it is possible to proactively identify groups of accident-prone commercial vehicle drivers and to implement effective measures to reduce their involvement in crashes.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".