Risk Assessment of Bus Drivers Considering Physical and Psychological Health Using a Risk Scoring Model
Bibliographic record
Abstract
Bus-related crashes lead to significant property damage, injuries, and fatalities. Assessing driver risk forms the basis for implementing safety countermeasures for crash-prone high-risk bus drivers. Physical and psychological health characteristics are critical factors in crash risk. However, little research has considered physical and psychological health in driver risk assessment. Most studies classify drivers into several risk groups based on a single indicator, but quantifying individual driver risk with multiple factors remains underexplored. This study developed a driver risk scoring model for bus drivers by simultaneously considering demographic characteristics, travel characteristics, driving behavior, and physical and psychological health, to explore significant variables influencing driving risk. The investigation, conducted at a bus company in Beijing, China, surveyed 10,201 bus drivers. A hybrid weight determination method, combining analytic hierarchy process and entropy weight, determined the weight of each variable. Higher driver scores indicated a lower level of driver risk. The frequency of crashes and violations was significantly negatively correlated with rating scores, validating the model’s effectiveness. Results revealed: (a) bus driver scores ranged from 33.5 to 100 points, with approximately 90% of drivers scoring over 80 points, categorizing them as a safe group, and (b) driving behavior and physical and psychological health were the most significant variables affecting bus drivers’ risk. This study acknowledges its reliance on self-reported data and limited generalizability outside of China, yet it provides valuable insights for improving driver risk assessment within similar contexts. The findings could assist bus companies in identifying high-risk drivers and selecting appropriate intervention methods.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".