Before–After Safety Evaluation Using Full Bayesian Macroscopic Multivariate and Spatial Models
Bibliographic record
Abstract
Many studies have addressed spatial correlation in traffic collision modeling. It has been generally concluded that the inclusion of spatial correlation improves model goodness of fit and the precision of parameter estimates. However, the application in before–after safety evaluation has rarely been documented in the traffic safety literature. The objectives of the presented study were to ( a) apply both the univariate and multivariate full Bayesian (FB) spatial models in before–after safety evaluation and ( b) compare the results with those of nonspatial FB models. A reduction of the posted speed limit in urban residential neighborhoods in Edmonton, Alberta, Canada was used as a case study for the before–after safety evaluation. Yearly collision data and other neighborhood characteristics data were collected for a group of treated and reference neighborhoods to develop macroscopic models. The four models considered in this study were ( a) Poisson–lognormal, ( b) Poisson–lognormal with conditional autoregressive (CAR) distribution, ( c) multivariate Poisson–lognormal, and ( d) multivariate Poisson–lognormal with CAR distribution. The results showed that the multivariate Poisson–lognormal with CAR distribution model for collision severities outperformed the other three models according to the deviance information criteria. Parameter estimates showed slight differences across the models. However, for the current data set, the results of the before–after safety evaluation showed similar findings across the models. Estimated collision reductions were 13%, 24%, and 12% for total, severe, and property-damage-only collisions, respectively.
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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 | 0.004 | 0.009 |
| 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".