Route Preferences among Adults in the near Market for Bicycling: Findings of the Cycling in Cities Study
Bibliographic record
Abstract
PURPOSE: To provide evidence about the types of transportation infrastructure that support bicycling. DESIGN: Population-based survey with pictures to depict 16 route types. SETTING: Metro Vancouver, Canada. SUBJECTS: 1402 adult current and potential cyclists, i.e., the "near market" for cycling (representing 31% of the population). MEASURES: Preference scores for each infrastructure type (scale from -1, very unlikely to use, to +1, very likely to use); current frequency of use of each infrastructure type (mean number of times/y). ANALYSES: Descriptive statistics across demographic segments; multiple linear regression. RESULTS: Most respondents were likely or very likely to choose to cycle on the following broad route categories: off-street paths (71%-85% of respondents); physically separated routes next to major roads (71%); and residential routes (48%-65%). Rural roads (21%-49%) and routes on major streets (16%-52%) were least likely to be chosen. Within the broad categories, routes with traffic calming, bike lanes, paved surfaces, and no on-street parking were preferred, resulting in increases in likelihood of choosing the route from 12% to 37%. Findings indicate a marked disparity between preferred cycling infrastructure and the route types that were currently available and commonly used. CONCLUSION: This study provides evidence for urban planners about bicycling infrastructure designs that could lead to an increase in active transportation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".