Integrating Public Transit and Shared Micromobility Payments to Improve Transportation Equity in Seattle, WA
Bibliographic record
Abstract
This study explores how shared micromobility services can integrate with public transit through equitable payment structures to address first and last mile issues for light rail transit riders in Seattle, WA, and increase accessibility for low-income households. Seattle has recently permitted shared micromobility services such as e-scooter companies to begin operating alongside existing bikesharing services in the city. However, equity concerns have arisen as the users of bikeshare have been disproportionately white, affluent, and well-educated. To address these concerns, efforts have been made to reduce barriers to access and make these services more equitable to encourage their use among marginalized populations. Previous research has demonstrated evidence that these services can improve accessibility for disadvantaged populations such as low-income people of color. This research consists primarily of a literature review of relevant academic and gray literature, and a jurisdictional scan of cities in the U.S., Canada, Finland, and China. The objective of this research is to identify barriers to accessing shared micromobility services and synthesize existing best practices to propose solutions to make these services more equitable. Findings from this research then inform a set of recommendations for equitable payment integration in King County, which can also be generalized to other municipalities that are striving for equitable public transit and shared micromobility integration.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".