Introducing a Framework for Cycling Investment Prioritization
Bibliographic record
Abstract
Concerns over urban congestion, air pollution, and health disparities have prompted many cities to expand their bicycle networks to foster a cycling culture. Given limited budgets, choosing a set of road segments for investments in cycling infrastructure to achieve a city’s ambitious goals is still a challenge. This study introduces a quantitative framework for prioritizing future bicycle improvement projects for implementation within budgetary constraints using different intervention strategies with regard to specific goals and objectives. The “connectivity-focused” prioritization strategy aims to consolidate bicycle networks and improve network connectivity. The “equity-based” strategy grows the bicycle networks and favors cycling equity goals; it prioritizes cycling projects that improve bicycle accessibility to urban opportunities for disadvantaged populations and mitigates disparities in access to bicycle facilities. The “modal shift” strategy seeks to build a bicycle network which would appeal to prospective bicycle commuters and thereby reduce vehicle kilometers traveled (VKT) and greenhouse gas (GHG) emissions. The results highlight the importance of growing bicycle networks in a systematic way and show that this framework is useful for guiding the implementation of new bicycle facilities with specific policy priorities.
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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 | 0.027 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".