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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".