Prediction of seasonal variation in traffic collisions on rural highways: A case study in the province of British Columbia
Bibliographic record
Abstract
Traffic collisions are one of the world's major problems. According to the World Health Organization (WHO), about 1.25 million people die every year in traffic collisions across the world and a further 20-50 million are injured or disabled. 24% of all collisions are weather-related. Collision risk usually increases from 50 to 100 percent during precipitation. Various tools/methods were developed in order to assess highway safety. Historically, collision rates, linear regression and generalized linear regression methods have been used as the basis for safety analysis. Research has shown that there are limitations with such approaches due to the non-linear relationship between collision frequency and exposure. Collision prediction modelling is the recommended technique for estimating road safety in the American Association of State Highway and Transportation Officials (AASHTO) Highway Safety Manual (HSM). However, the prediction modelling does not really take into consideration of traffic seasonal variation and weather impacts as the annual average daily traffic (AADT) is one of main variables having a direct impact on safety. Previous studies indicate that weather especially winter weather is associated with traffic collisions. This study analyzed the seasonal variations of traffic and collisions on rural highways in British Columbia, Canada. Collision risks related to winter weather were investigated and assessed. It concludes that traditional techniques of highway safety assessment without the consideration of seasonal variation of traffic collisions, especially impacts of winter condition in Canada, might result in underestimating the collision risk. This paper suggests that further study of highway safety with a focus on the seasonal variation of collisions and traffic volumes will help to improve the highway safety assessment and provide valuable inputs for winter road maintenance. Furthermore, it also suggests a real need for an interdisciplinary approach in highway safety assessment in order to integrate all factors including human, vehicle, traffic and road/environment and provide a clear and comprehensive understanding of causes of collisions. Other countermeasures, including new material applications in pavement, intelligent transportation systems (ITS) and winter maintenance methods/strategies, in addition to traditional methods (Snow plowing, sanding and salting) of preventing traffic collisions during winter should also be considered.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".