Analysis of Factors Affecting Winter Collision Severity
Bibliographic record
Abstract
This paper presents the results of an analysis aiming at identifying the main injury severity factors associated with road collisions that occur during snowstorms, including traffic conditions, road geometry and environment, pavement surface conditions as well as vehicle and driver characteristics. A multilevel multinomial logit model is introduced for capturing the hierarchical nature of the collision data between individual collisions and the vehicles and persons involved. Different from past studies, the modeling effort focuses on the collisions that occurred over snowstorms so that the effect of weather related factors are not masked due to the imbalance of data sample between collisions occurred under normal conditions and those under snowstorms. This approach is also necessary for ensuring that the incremental effect of different weather severity, as well as winter road maintenance operations, could be captured. Collisions that occurred on a number of highway routes from the province of Ontario, Canada, over six winter seasons (2000-2006), were selected for this analysis. It was found that factors related to drivers (age, sex, condition), road characteristics (number of lanes, speed limit, road surface conditions), vehicle type, position in vehicle, use of safety belt, and traffic volume have statistically significant effects on collision severity outcome. In general, the modeling results indicate that good road surface conditions, high traffic volume, young and male drivers and new vehicles are associated with reduced injury severity levels. The authors' analysis, however, did not confirm the main finding from literature, that is, severer weather, such as higher precipitation intensity and wind speed, is associated with lesser collision severity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".