Safety Evaluation of Offset Improvements for Left-Turn Lanes
Bibliographic record
Abstract
This study is a safety evaluation of offset improvements for left-turn lanes, a treatment intended to reduce the frequency of crashes by providing better visibility for drivers who are turning left. Geometric, traffic, and crash data were obtained for installations in Nebraska, Florida, and Wisconsin and for a number of untreated reference sites in each state. To account for potential selection bias and regression to the mean, an empirical Bayes before–after analysis was conducted. There was a large difference in observed effects in the three states, which may be explained, in part, by the variety of offset improvements applied. Florida and Nebraska employed pavement-marking adjustments or minor construction to improve the offset, but most improvements did not result in a positive offset. Wisconsin, conversely, reconfigured left-turn lanes through major construction projects and realized significant positive offsets. Wisconsin showed significant reductions in all crash types investigated (total reduction, 34%; injury, 36%; left turn, 38%; and rear end, 32%), while results in Florida and Nebraska showed little or no effect on total crashes. For Nebraska, however, a disaggregate analysis did reveal that the percentage reduction in crashes increases as the expected number of crashes increases. An economic analysis indicated that offset improvement through reconstruction is cost-effective at intersections with at least nine expected crashes per year and in which left-turn lanes are justified by traffic volume warrants.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".