Effectiveness of Green, High-Visibility Bike Lane and Crossing Treatment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it