Park ‘n’ Roll: Identifying and Prioritizing Locations for New Bicycle Parking in Québec City, Canada
Bibliographic record
Abstract
Promoting active modes of transportation, such as cycling, is an ongoing challenge faced by many cities around the world. Fostering a bicycle culture in an auto-dominant region is riddled with challenges, but success has been achieved with investments in bicycle infrastructure, including bicycle parking. This study presents a new methodology to identify the optimal locations to install short-term (bicycle racks) and long-term (bicycle lockers or indoor locking facilities) bicycle parking using a GIS-based approach that considers multiple criteria. Using Québec City, Canada, as a case study, our methodology considers multiple criteria related to the demand for bicycle parking, including the destinations of existing and potential cyclists and proximity to a frequent bus service. A prioritization index is developed to identify the optimal locations for long-term and short-term bicycle parking. This is followed by a recommendation of the number of bicycle parking spaces required to meet existing and potential demand. This paper aims to provide practitioners with an easy-to-use method to aid in the planning of new bicycle parking infrastructure, which is designed to be flexible and adaptable to other contexts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".