Fatality Risk of Intersection Crashes on Rural Undivided Highways in Alberta, Canada
Bibliographic record
Abstract
Intersections are recognized as the most hazardous locations on roads since conflict possibilities are high at intersections and often result in a high frequency of fatal crashes. A significant share of fatal crashes in Canada occur at intersections on rural undivided highways. Nevertheless, few studies have examined the factors contributing to the fatality risk of intersection crashes in Canada. In this study, a logistic regression model was applied to a sample of crash data at intersections on the rural undivided highways of Alberta, Canada, to investigate 18 factors and 71 variables. Of the significant factors, the major ones affecting the likelihood of fatality are the type of intersection, horizontal and vertical alignment of the highway at the intersection, signalization at the intersection, type of collision, impairment of drivers, and age of drivers involved in crashes. The fatality risk of intersection crashes tends to increase when crashes occur at offset intersections or at cross or T-intersections on horizontal curves. The likelihood of fatality tends to increase if the intersection is on a sag curve or at a constant grade. However, signalization at intersections tends to reduce the likelihood of fatality. Pedestrian-involved collisions, head-on collisions, right-angle collisions, and run-off-road collisions that involve hitting a fixed object and overturning of vehicles are associated with higher fatality risk. An intersection crash also tends to have a higher likelihood of fatality when it involves an older driver (>70 years) or impaired (by alcohol or drugs) or fatigued drivers.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| 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".