Secure Investment for active transport willingness to pay for secured bicycle parking in Montreal, Canada
Bibliographic record
Abstract
Fear of bicycle theft and related vandalism discourages bicycle usage. The present study recognizes this problem and aims to understand whether or not users are willing to pay for secured bicycle parking (SBP) in Montreal, Canada by examining the following research questions: 1) Are users willing to incur some of the extra cost of improving bicycle parking infrastructure? 2) Of those willing to pay, what are their common characteristics? and 3) Is there a distinction between those who are willing to pay and those who are able to pay? Results from a bilingual (English/French) online bicycle theft and parking survey provided 1,533 responses about cyclists’ willingness to pay for (SBP). Forty-three percent would be willing to pay at least $0.50/day for SBP, and the highest daily amount that some participants are willing to pay is $15.00. Findings from this study demonstrate that cities will benefit from improving their cycling infrastructure by installing SBP facilities and cyclists who state that risk of theft influences their decision to cycle are more likely to pay for SBP. The results show that pricing of SBP facilities can be an option, yet should stay low to ensure that security provided by paid bicycle parking always remain an incentive to use a bicycle.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.001 |
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".