Exploring the geographical equity-efficiency tradeoff in cycling infrastructure planning
Bibliographic record
Abstract
Cycling is affordable, healthy, and sustainable, but access to destinations on low-stress safe cycling routes in most cities is both limited and unevenly distributed. Many cities are expanding cycling networks to improve safety, increase cycling mode share, and increase diversity in access to cycling, however resources remain limited which requires prioritization of infrastructure. When proposed infrastructure locations are optimized to provide the highest average access to opportunities using a utilitarian definition of accessibility, marginalized groups and locations may be further left behind. This occurs since the greatest gains to network connectivity, using a utility definition, come from expansions inside or directly adjacent to the densest network areas. We compare utilitarian and equity-driven planning strategies for cycling network expansion and explore tradeoffs in spatial coverage, equity, and efficiency, using Toronto, Canada as a case study. We find that optimizing accessibility in several small regions instead of city-wide leads to an infrastructure plan that is more spatially dispersed. Further, we show that an optimization model targeting low-access areas produces an infrastructure plan with more regions meeting a minimum threshold of accessibility but with lower average accessibility gains, indicating the presence of an equity-efficiency tradeoff. We also find that infrastructure projects that maximize a region's accessibility to jobs are often located outside that region, challenging political perceptions of "local" infrastructure and benefits. These results inform planning, advocacy, design, and policy, and shed light on spatial and socio-demographic equity tradeoffs in deciding where to add cycling infrastructure.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".