Multivariate Full Bayesian Hot Spot Identification and Ranking
Bibliographic record
Abstract
The present study introduces a new technique for multivariate identification and ranking of hot spots based on the Mahalanobis distance. This approach aims to extend the univariate potential for safety improvement to cases in which multiple response variables are modeled jointly. Because the literature shows that ranking techniques based on Bayesian methods are superior to those that rely simply on the observed collision count, the proposed method was developed in a full Bayesian (FB) context. The new technique involves the following steps: (a) applying multivariate Poisson–lognormal regression models to the data by means of the FB method, (b) using the estimates of the Poisson posterior means for each site to compute the multivariate (Mahalanobis) distance from what is the normal Poisson mean for similar sites, and (c) preparing an ordered list of potentially hazardous sites. This method was applied to a sample of 173 signalized intersections in the city of Vancouver, British Columbia, Canada, for the years 2008 to 2012. The study also examines the consistency of the technique itself by analyzing the mathematical intersection of ranked sites identified in subsequent time periods. Finally, the consistency of the multivariate FB ranking was assessed against the independent (separate) univariate one that is still dominant in road safety evaluations.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".