Quantifying the effectiveness of an active treatment in improving highway-railway grade crossing safety in Canada: an empirical Bayes observational before–after study
Bibliographic record
Abstract
Transportation engineers face challenging safety investment decisions, particularly for highway-railway grade crossings (HRGCs), where rare collision occurrences and incomplete historical records complicate the assessment of countermeasure cost-effectiveness. This study introduces an empirical Bayes (EB) observational before–after approach to address these challenges, specifically examining the impact of a widely used safety countermeasure: flashing lights, bells, and gates (FLBG). The research covers a total 171 FLBG crossings with pre-implementation conditions varying between standard reflective crossing signs and flashing lights and bells, utilizing collision data from 2000 to 2012 for analysis. This study is the first to substantiate quantitatively the effectiveness of FLBG treatment in improvising the safety of HRGCs and show that the effect is dependent of crossing characteristics, such as track angle, pavement conditions, and train speed limits. These findings can provide valuable support to decision-makers, enabling them to make more effective decisions for grade crossing safety improvement projects.
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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 | 0.031 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".