Investigation of the Effect of Super Sharrows on Cyclist and Vehicle Behavior
Bibliographic record
Abstract
This study examined the potential effect of special paintings of shared lane markings (super sharrows) on a number of operational and safety performance parameters for cyclists and motor vehicles. These performance parameters were used to assess pretreatment and posttreatment behavior when cyclists and motor vehicles were near one another. The performance parameters were ( a) rate of lane change maneuvers performed by vehicles in the presence as well as the absence of cyclists and ( b) lateral spacing between cyclists, vehicles, and curb edge. In general, the main objectives of this treatment were ( a) providing cyclists with comfort by allowing them to ride in the middle of the travel lane and ( b) promoting safe passing by motor vehicles. The effect of the super sharrows on cyclists and motor vehicles was analyzed with statistical analysis by comparing pretreatment and posttreatment conditions. The key findings are as follow: ( a) super sharrows had an effect on motor vehicle lane change maneuvers, represented by an increase in the percentage of motor vehicles that changed from the right lane (location of super sharrows) to the left lane with the presence of a cyclist on the right lane; ( b) the number of motor vehicles that changed from right lane to left lane and back to right lane in both full and partial encroachment into the left lane decreased; ( c) the number of the motor vehicle lane change maneuvers from left to right lane decreased; and ( d) cyclists were found to be riding farther from the right curb with the presence of the super sharrows.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".