Development of Planning-Level Transportation Safety Models using Full Bayesian Semiparametric Additive Techniques
Bibliographic record
Abstract
Recently, several attempts have been made to develop collision prediction models in which spatial dependency is considered. These models recognize the local nature of spatial data by relaxing the regression analysis assumption that the error terms for each observation are independent. The primary objective of this study is to investigate an alternative technique for capturing the spatial variations in the relationship between the number of zonal collisions and potential transportation planning predictors. Spatial relationships are incorporated into the full Bayesian semiparametric additive modeling framework through the covariance of the error terms. The secondary objective of this research study is to build on knowledge of comparing the accuracy of full Bayesian models to that of generalized linear and geographically weighted Poisson regression models. The spatial covariates from the full Bayesian semiparametric additive model indicate that collision frequencies in traffic analysis zones are spatially correlated. The results of accuracy comparison indicate that the spatial models perform better than the conventional generalized linear models. However, mixed results are obtained when the FBSA models were compared to the geographically weighted Poisson regression models.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".