Testing a generalized model to map bicycle ridership using crowdsourced data in a racially diverse city
Bibliographic record
Abstract
Bike counts are important for measuring safety and prioritizing infrastructure investment. Crowdsourced data, generated by the bicycle app Strava, provides bike counts at unprecedented resolutions and extents for the sample of bicycle counts where the riders are contributing data. Researchers have developed models to expand the sample of bicycle counts measured in crowdsourced data to all bicycle counts. However, these models were developed and tested in cities with predominantly white populations, and it is unknown how well these models apply to cities with racially diverse populations. In this work, we tested expansion methods in Oakland California, one of the most racially diverse cities in the United States. We built a bike count and geographic predictor data set, collected new count data to improve representativeness of training data, used lasso regression to select and build regression models, and compared the results to the cities previously published. We found that the variables selected and accuracy of estimation of average annual daily bicyclists (AADB) by road segment aligned with other cities. Our findings, that crowdsourced data can adequately represent bicycling in racially diverse cities, will allow crowdsourced data to be used in racially diverse cities, which are often underserved by transportation infrastructure.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".