Exploring the Utilitarian and Non-Utilitarian Bicycling Behaviors of North American Women Cyclists
Bibliographic record
Abstract
Background:Increasing bicycling is a common goal to increase physical activity. Studies have shown that women bike at a lower rate than men due to various factors; few studies have examined attitudes and perceptions of women cyclists at a large scale. This study aims to fill this gap by examining the bicycling behaviors of women cyclists across North America.Methods:We analyzed an online survey of 1,868 women cyclists in the US and Canada. The survey recorded respondents’ bicycling skills, attitude, perceptions of safety, surrounding environment, and other factors that may affect the decision to bicycle for transport and recreation. We used chi-squared and non-parametric tests to examine the differences among groups of cyclists. We then utilized tree-based machine learning methods (e.g., bagging, random forest, boosting) to select the most common motivations and concerns of these cyclists.Results:We found that perception of safety (e.g., traffic, motorist behavior, lighting) were significantly different among women cyclists across age groups, bicycling skill, and education levels. Tree-based model results indicate that perceptions of safety, wayfinding, bicycle facilities, hills, and concerns about health were among the most important factors for women to bike for transport or recreation. The average classification error rates were around 9-12%. Cross validation performed on 20% of the sample resulted in moderate error rates (9-25%).Conclusions:Our study suggests opportunities for designing healthy cities for women. Cities may enhance safety to increase bicycling rates of women through investing in bicycle facilities and lighting infrastructure as well as enforcing speed limits and aggressive behavior from motorists. Our study also outlines how promotional and education materials designed for women in the non-bicycling group may help to change behavior. Future studies to compare bicycling behaviors of cyclists and non-cyclists of both genders would be useful.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".