Identifying areas of high risk for collisions: A Canda-wide study of grade crossing safety
Bibliographic record
Abstract
Ranking sites and identifying high-crash risk locations based on various safety performance measures (e.g. expected crash frequency) are among the key tasks of the safety management program, enabling an effective allocation of funds for safety improvement projects. While several studies have discussed the issues relating to the hotspot identification process at a micro-level (e.g., intersections or highway segments), less attention is given to the macro-level hotspot identification issue: how to identify areas or regions with the highest risk of crashes. In this research, we introduce a Bayesian multilevel (hierarchical) model for estimating the regional differences while controlling for other important site attributes. The proposed method is illustrated using a case study on railway grade crossings in Canada. While accommodating the spatial dependencies of crash risk, our method allows a fair comparison of different regions by adjusting for the effect of covariates such as traffic exposure. In particular, we compute pairwise probabilities of crash risk for each province in Canada compared to all others. We are therefore able to draw inferences about regional safety performances under similar circumstances. Our findings indicate the need for further investigation to identify the possible reasons for inter-region variations in grade crossing safety across Canada. Our approach could be useful to guide safety policy development and resource allocation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".