Assessing the Effect of Weather States on Crash Severity and Type by Use of Full Bayesian Multivariate Safety Models
Bibliographic record
Abstract
Rather than investigate the isolated effects of individual weather elements on crash occurrence, this study investigated the aggregated effect of weather states, defined as a combination of various weather elements (e.g., temperature, snow, rain, and wind speed), on crash occurrence. The main argument was that a combination of weather elements might better represent a particular weather condition and subsequent safety outcome. Therefore, to explore the effect of various weather states on crash severity and type, this study defined 12 weather states, based on temperature, snow, rain, and wind speed, and developed multivariate safety models by using 11 years of daily weather and crash data for Edmonton, Alberta, Canada. The proposed models were estimated in a full Bayesian context via a Markov chain Monte Carlo simulation, while a posterior predictive approach assessed the models’ goodness of fit. Results suggested that property-damage-only (PDO) crashes increased by 4.5% to 45% because of adverse weather states and showed that PDO crashes were more affected by adverse weather states than were severe (injury and fatal) crashes. For crash type, adverse weather states were associated with an increase of 9% to 73.7% for all crash types, with the highest increase for run-off-the-road crashes. Duration of daylight was found to be significant and negatively related to all crash types and PDO crashes. Sudden weather changes of major snow or rain were statistically significant and positively related to all crash types. Days of the week and seasons of the year were used as dummy variables and were statistically significant in relation to crash occurrence.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".