Injury risks in collisions involving buses in Alberta
Bibliographic record
Abstract
This paper investigated the factors that contributed to injury in collisions that involved at least one bus in the province of Alberta. In this study, all kinds of buses (i.e. school bus, transit bus, intercity bus and other bus) crashes were considered. Four separate logistic regression models were calibrated: 1. single vehicle collisions on highways; 2. single vehicle collisions in non-highway locations; 3. two vehicles collisions on highways; and 4. two vehicles collisions on non-highway locations. The contributing factors to collision severity examined were: weather condition, characteristics of collision partner, collision partner's driver age, bus driver age, bus, age, grade and sag sections, lighting conditions and collision location. Our analysis showed that weather condition was a significant contributing factor in all four types of collisions. Interestingly, adverse weather condition resulted in fewer injuries. Our results also showed that types of collision, characteristics of collision partner, collision partner's driver age and weather condition had significant effect on severity level for collisions occurring on both highway and non-highway locations. Additionally, the driver age of bus and collision partner were found to be significant factors in collision severity. Other factors were shown to affect injury risk only in one particular situation. For instance, for highway related collisions, driver age of collision partner had significant effect on severity levels whereas the age of the bus driver didn't. In addition, for highway collisions, collision severity was higher for head-on crashes, bus-bus crashes, bus-truck crashes, bus-motorcycle crashes, older buses, on grade and in sags, during dark and sun glare whereas probability decreased with larger outside shoulder width. For non-highway locations, crashes occurring near tunnel/underpass/overpass/signalized intersection were shown to result in higher probability of injury. Our results also showed that pedestrian involved single-bus collisions on non-highway road had higher injury risk than involvement of any other objects.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".