Causal Impacts of Protected Bike Lanes on Cycling Behavior with Demographic Disparities
Bibliographic record
Abstract
Cities around the world face significant barriers to grow urban cycling, including competing budgetary priorities and car-centric streets. Thus, when making decisions regarding the installation of bicycle infrastructure, it is crucial to understand if and to what extent different bicycle-lane types increase bicycle ridership. However, associations between bicycle infrastructure and bicycle ridership have primarily been studied in the context of individual lanes and corridors, or when analyzed at the scale of entire cities, generalized across different bike-lane types. Drawing upon 72 million bikeshare trips from Citi Bike in New York, we demonstrate that there is an approximately 18% increase in bikeshare trips at adjacent stations in the 12 months following the installation of protected bike lanes (those with a physical barrier between cyclists and automobile traffic) and a 14% increase associated with painted bike lanes (where a line of pavement marking is present) and `sharrows' (where a normal traffic lane is marked with a bike stencil). However, using a difference-in-differences analysis, we detect a causal effect on bikeshare ridership only following the installation of protected bike lanes, with an average monthly increase of 379 rides per station (p<0.001). Despite this causal effect being pronounced among census block groups with higher percentages of older adults (688 rides per month per station, p<0.001), the causal effect of protected bike lanes on bikeshare ridership is absent in census block groups where the percentage of Black residents is medium to high. Taken together, these findings indicate that planners must emphasize protected bike lanes to spur ridership, and simultaneously target policies and programming to communities of color, to ensure that such infrastructure makes urban cycling a viable option for all residents.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".