Safety Effectiveness of High Friction Surface Treatment at Signalized Intersections in British Columbia
Bibliographic record
Abstract
High friction surface treatment (HFST) is a pavement and safety treatment that dramatically and immediately increases pavement friction to reduce crashes, injuries, and fatalities associated with friction demand issues. Understanding the effectiveness of HFST as a safety measure is crucial for estimating the expected crash reduction and evaluating the cost-effectiveness of future HFST implementations. Existing research on HFST safety effectiveness evaluation is limited to horizontal curves and ramps, despite the promising safety benefits of installing HFST at other locations, such as signalized intersections. To help fill this research gap, this paper presents a rigorous before-and-after safety effectiveness evaluation of HFST installation at signalized intersections using traffic and crash data obtained from 15 treatment sites and 90 control sites in British Columbia, Canada. To enhance the validity of the safety assessment, two before-and-after evaluation methods were applied: empirical Bayes and full Bayes. The results indicated statistically significant safety benefits of HFST at the treated sites. Specifically, the estimated reductions in serious (fatal and injury) crashes, serious rear-end crashes, and serious wet-pavement crashes, are about 51%, 57%, and 64%, respectively. It is worth noting that an unexpected decline in crashes was observed at the control sites, which introduces some uncertainty in interpreting the results and warrants careful consideration.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".