Safety Evaluation of Stop Sign In-Fill Program
Bibliographic record
Abstract
This paper describes a project undertaken to evaluate the safety impacts associated with the Stop Sign In-Fill (SSIF) program. The SSIF program was launched by the Insurance Corporation of British Columbia (Canada) in 1998 and consists of installing stop signs alternately at every second intersection in residential neighborhoods in the Greater Vancouver Regional District. The main objective of the program is to reduce the frequency and severity of collisions and thereby reduce insurance claims costs in addition to providing a traffic-calming effect on residential neighborhoods. The evaluation included a time series analysis to investigate the effectiveness of the SSIF program on road safety performance at 380 intersections. The evaluation used comparison groups and three techniques to determine the safety impacts of the SSIF program. The first two techniques are based on the odds ratio methodology, while the third is based on the likelihood method. The results of the three techniques were consistent and showed that injury collisions were reduced 61% to 72%, while total collisions were reduced 45% to 55%. It was concluded that the installation of stop signs at uncontrolled intersections in residential neighborhoods was an effective measure for reducing both the frequency and severity of collisions in urban areas.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".