Does One Bicycle Facility Type Fit All? Evaluating the Stated Usage of Different Types of Bicycle Facilities among Cyclists in Quebec City, Canada
Bibliographic record
Abstract
For cities wishing to foster a strong culture of cycling, developing a network of safe and efficient bicycle infrastructure is paramount, yet not a straightforward task. Once transport professionals have selected the optimal location for a new bicycle facility, determining the optimal facility type is imperative to ensure that the new infrastructure encourages cycling trips and increases the safety of cyclists. The present study presents a nuanced approach to evaluating cyclists’ usage of various types of bicycle facilities. To achieve this goal, we employed survey data of cyclists in Quebec City, Canada, to study how many cyclists reported using a particular bicycle facility in the survey against their reasonable access to those reported facilities. To account for different preferences, behavior, and motivations among cyclists, we segmented our study sample into six types of cyclist. Finally, regression modeling was employed to predict the stated usage of three facility types present in the study area (recreational path, bi-directional protected lane, and painted lane), while controlling for access to this path, cyclist type, and personal and neighborhood characteristics. Results indicate that if a cyclist has access to each facility type on their commute, they are most likely to use a recreational path on their commute, followed by a painted bicycle lane. Respondents with access to a bi-directional lane are no more likely to report using this facility than respondents without access. Overall, this study is intended to encourage a dialog between cyclists and transport practitioners to uncover the factors contributing to effective bicycle infrastructure.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".