How Pavement Markings Influence Bicycle and Motor Vehicle Positioning
Bibliographic record
Abstract
The purpose of this study was to determine how pavement markings influence bicyclist and motorist positioning, particularly how far bicyclists travel from parked cars. The research examined the effects of the sequential addition of the component markings of a bicycle lane on a road with on-street parking in Cambridge, Massachusetts. The data measured were the distance that cars parked from the curb, the distance that bicyclists rode from the curb, and the distance that traveling motor vehicles drove from the curb. Data on bicyclists and moving motor vehicles were gathered by videotaping. The three pavement marking treatments–-an edge line demarcating the travel lane, the edge line and bicycle symbols, and a full bicycle lane–-were all effective at influencing bicyclists to ride farther away from parked cars than when no pavement markings were present. All three treatments significantly increased the percentage of cyclists riding more than 9 and 10 ft from the curb; these distances were used as benchmarks for where cyclists should ride to be farther from the opening-door zone of a parked car. There was variation between the signalized and the uncontrolled intersections. Before-and-after intercept surveys of cyclists and motorists were administered. In the before survey, cyclists most often responded that the best way to improve bicycling on Hampshire Street was to add bicycle lanes. Cyclists also rated the full bicycle lane most favorably in the after survey. There was no change in cyclist comfort levels between the before and the after surveys. When motorists were asked what made them most aware of cyclists on the street; the most common response in the before survey was “nothing.” In the after survey, the most common response was “the bicycle lane.”
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".