Evaluating Impact on Safety of Improved Signal Visibility at Urban Signalized Intersections
Bibliographic record
Abstract
A study evaluated the safety impacts associated with improved signal visibility at urban signalized intersections. The improvements included one or a combination of the following upgrades: signal lens size, new backboards, reflective tapes added to existing backboards, and additional signal heads. Intersection collision data based on insurance claim records from the Insurance Corporation of British Columbia were used in the study to evaluate the effectiveness of the signal visibility improvement. These automobile insurance claim data are current, comprehensive, and considered quite reliable for intersection locations. Traffic volume and collision data were collected for treatment and comparison groups. The treatment group included 139 intersections and the comparison group included 85 intersections. The data for the comparison group were used to account for history and maturation confounding factors. An empirical Bayes analysis was used to ensure that the evaluation results were reliable and to account for the regression-to-the-mean confounding factor. The analysis was undertaken for both severe (injury + fatal) and property-damage-only (PDO) collisions and also for daytime and nighttime collisions. The evaluation results indicate statistically significant reductions of 8.5%, 5.9%, 6.6%, and 7.3% for PDO, daytime, nighttime, and total collisions, respectively. Severe collisions showed a nonsignificant reduction of 2.6%.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".