Local Governments Adopted Strategies to Improve Shared Micromobility Infrastructure
Bibliographic record
Abstract
Shared micromobility (bikesharing and scooter sharing) experienced market growth since 2021, rebounding from the pandemic across markets in the US, Mexico, and Canada. In partnership with the North American Bikeshare and Scootershare Association (NABSA) and Toole Design, researchers at the Transportation Sustainability Research Center (TSRC) at UC Berkeley have collaborated on the data collection and analysis of the shared micromobility industry metrics through a series of annual reports beginning in 2019. This includes a series of operator and agency surveys.1 Most recently, TSRC researchers collaborated on an Operator Survey (n=29) and an Agency Survey (n=52), distributed between January 2023 and June 2023, of all known shared micromobility operators and agencies as part of the 2022 state-of-the-industry report. Similar surveys were deployed in January 2022 and May 2022. These surveys include questions about shared micromobility systems2 operating within those agency jurisdictions and operator markets.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".