A random parameter bivariate approach for modelling freeway crash frequency by severity level
Bibliographic record
Abstract
This study proposes a method for jointly modelling the frequencies of freeway traffic crashes with varying severities. The proposed approach employs a Poisson-lognormal model that treats property damage-only (PDO) crash frequencies and killed or injured (KOI) crash frequencies as two dependent variables while accounting for the correlation of random effects across different severities. To address spatial effects and heterogeneity within the data, a bivariate mixed model is developed, integrating spatial spillover effects and a two-dimensional conditional autoregressive prior. Furthermore, a second bivariate mixed-effects model incorporating random parameter terms is constructed. The empirical findings reveal a significant correlation between spatial effects influencing PDO and KSI crash frequencies. The random parameter model, which simultaneously accounts for spatial correlation and spillover effects, demonstrates superior goodness-of-fit and predictive performance compared to alternative models. Based on the parameter estimates derived from the optimal bivariate model, tailored freeway traffic safety improvement measures are proposed.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".