If we build it, who will benefit? A multi-criteria approach for the prioritization of new bicycle lanes in Quebec City, Canada
Bibliographic record
Abstract
Many cities across the world are actively promoting cycling through investments in cycling infrastructure, yet ensuring that the benefits from these investments are distributed equally in a region and not benefiting only one group is an important social goal. The aim of this study is to develop a methodology that can help in identifying where new bicycle facilities can be built in a region while prioritizing investments for those who need them most. The study uses Quebec City, Canada, as an example since the city has recently made a strong commitment to provide safe and attractive bicycle infrastructure to its residents. It also uses a GIS-based grid cell model to identify priority areas for cycling investment in different parts of the city. This is followed by a proposal for a new set of facilities based on a multi-criteria approach. These proposed facilities are then evaluated through a level of usage analysis to determine which routes will provide the maximum benefit to existing and potential cyclists. Finally, an equity analysis is conducted to evaluate whether the new facilities will meet some of the travel needs of individuals residing in socially deprived neighborhoods. This step in the evaluation process proposes a new social equity component in bicycle planning processes. This research can be of value to planners, engineers and policymakers working toward investments in bicycle facilities because it shows the full process of planning and evaluating different cycling facilities while incorporating social equity principles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".