Effectiveness of Green, High-Visibility Bike Lane and Crossing Treatment
Bibliographic record
Abstract
Recently, colored bicycle lane treatments have been implemented to heighten awareness of bicycle lanes and crossings in cities around Europe, Canada, and the United States. This study focuses on evaluating the effectiveness of a new, green, high-visibility bicycle lane and crossing treatment located on a cloverleaf interchange in South Burlington, Vermont. To do this, the study monitored two treated and two control crossings, including the road segments before and after the crossings. The chosen sites were monitored using both visual and video surveillance, for a total of 56 hours in the summer of 2004 and an additional 32 hours in the summer of 2005. Observed bicycle behavior included bicyclist position before and after crossing the on/off ramps, bicyclist position while crossing the on/off ramps, riding travel direction, bicyclists’ stopping behavior, and motorists’ stopping and yielding behavior. Surveys were also developed for bicyclists and motorists, and distributed both over the internet and in person. Information from the field observations and from the survey responses was compiled and synthesized to determine how effectively the green bicycle lanes and crossings encouraged lower levels of conflict, higher motorist and bicyclist awareness, and better adherence to traffic regulations. Among the conclusions of the study is that the green bike lane treatment encouraged a majority of bicyclists, especially those riding legally in the direction of traffic, to use the bike lane over the sidewalk or the road. The treatment did not, however, encourage motorists to yield more often to cyclists at the crossings.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".