Assessing Time and Weather Effects on Collision Frequency by Severity in Edmonton Using Multivariate Safety Performance Functions
Bibliographic record
Abstract
Weather factors have been identified by many as one of the major environmental risks that are known to have a significant effect on collision occurrence. To investigate the impact of weather-related factors on collision risk, collision data by frequency and severity were combined with weather-related information from Environment Canada Weather Office in this study for statistical analysis. Considering the multivariate nature of the data, a multivariate model with a multiple regression link is proposed to use the number and severity of collisions as a representative outcome variable with weather-related data and a proxy of exposure added as independent variables. The multivariate model was found to predict collisions with high precision. Also, the results indicated a high correlation between severe and property-damage-only collisions which demonstrates that higher property-damage-only collisions are associated with higher severe collisions, as the collision likelihood for both levels is likely to rise due to same weather conditions, similar deficiencies in roadway design and/or other unobserved factors. For severe collisions, there was a significant declining annual trend, significant decrease during weekends and holidays, significant inverse relationship with mean temperature and a significant positive relationship with total snow fall and total precipitation. On the other hand, for property-damage-only collisions, there was a significant growing annual trend, significant decrease during weekends and holidays, significant inverse relationship with mean temperature and a significant positive relationship with total and previous snow fall and total precipitation. It is worth mentioning that the above results reflect the analysis of daily collision and weather data for the entire City of Edmonton (Alberta, Canada) over a course of 11 years.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".