Lessons from the Green Lanes: Evaluating Protected Bike Lanes in the U.S.
Bibliographic record
Abstract
This report presents finding from research evaluating U.S. protected bicycle lanes (cycle tracks) in terms of their use, perception, benefits, and impacts. This research examines protected bicycle lanes in five cities: Austin, TX; Chicago, IL; Portland, OR; San Francisco, CA; and Washington, D.C., using video, surveys of intercepted bicyclists and nearby residents, and count data. A total of 168 hours were analyzed in this report where 16,393 bicyclists and 19,724 turning and merging vehicles were observed. These data were analyzed to assess actual behavior of bicyclists and motor vehicle drivers to determine how well each user type understands the design of the facility and to identify potential conflicts between bicyclists, motor vehicles and pedestrians. City count data from before and after installation, along with counts from video observation, were used to analyze change in ridership. A resident survey (n=2,283 or 23% of those who received the survey in the mail) provided the perspective of people who live, drive, and walk near the new lanes, as well as residents who bike on the new lanes. A bicyclist intercept survey (n= 1,111; or 33% of those invited to participate) focused more on people’s experiences riding in the protected lanes. A measured increase was observed in ridership on all facilities after the installation of the protected cycling facilities, ranging from +21% to +171%. Survey data indicates that 10% of current riders switched from other modes, and 24% shifted from other bicycle routes. Over a quarter of riders indicated they are riding more in general because of the protected bike lanes. A large majority of drivers and bicyclists stated that they understood the intent of the intersection designs and were observed to use them as intended, though specific designs perform better than others on certain tasks. No collisions or near-collisions were observed over 144 hours of video review for safety at intersections, including 12,900 bicyclists. Residents and bicyclists indicated that any type of buffer shows a considerable increase in self-reported comfort levels over a striped bike lane, though designs with more physical separation had the highest scores. Buffers with vertical physical objects (those that would be considered protected lanes - e.g. with flexposts, planters, curbs, or parked cars) all resulted in considerably higher comfort levels than buffers created only with paint. Flexpost buffers got very high ratings even though they provide little actual physical protection from vehicle intrusions— cyclists perceive them as an effective
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".