Accident Prediction Models for Winter Road Safety
Bibliographic record
Abstract
Most accident prediction models are developed with single-level count data models, such as the traditional negative binomial models with fixed or varying dispersion parameters, assuming independence of data. For many accident data sets in road safety analysis, especially those that are highly disaggregated (hourly data), a hierarchical structure in the data often manifests in some form of correlation. Crash prediction models developed with aggregate data could produce biased results because of the assumption of data independence and inflation of the adequacy of the model's explanation because of the use of aggregate data. The potential effects of data aggregation and correlation on accident prediction models are investigated. The analysis uses an accident database that includes hour-level and storm-level accident counts for individual winter snowstorms at four highway sections in Ontario, Canada. Models of two levels of aggregation, aggregated event-based models and disaggregated hourly based models, were developed. The effect of data aggregation had a significant effect on model results, whereas the difference between conventional regression and multilevel regression was inconsequential.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".