Bibliographic record
Abstract
This study investigated the effects on bikesharing ridership levels of demographic and built environment characteristics near bikesharing stations in three operational U.S. systems. Although earlier studies focused on the analysis of a single system, the increasing availability of station-level ridership data has created the opportunity to compare experiences across systems. In this study, particular attention was paid to data quality and consistency issues raised by a multicity analysis. This project also expanded on earlier studies with the inclusion of the network effects of the size and spatial distribution of the bikesharing station network, which contributed to a more robust regression model for the prediction of station ridership. The regression analysis identified a number of variables that had statistically significant correlations with station-level bikesharing ridership: population density; retail job density; bike, walk, and transit commuters; median income; education; presence of bikeways; nonwhite population (negative association); days of precipitation (negative association); and proximity to a network of other bikesharing stations. Proximity to a greater number of other bike-sharing stations exhibited a strong positive correlation with ridership in a variety of model specifications. This finding suggested that, with the other demographic and built environment variables controlled for, access to a comprehensive network of stations was a critical factor to support ridership. Compared with earlier models, this model is more widely applicable to a diverse range of communities and can help those interested in the adoption of bikesharing systems to predict potential levels of ridership and to identify station locations that serve the greatest number of riders.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".