Bike Share: A discussion and case study analysis Including recommendations for Cal Poly and the City of San Luis Obispo
Bibliographic record
Abstract
Since 2015, micromobility has swiftly expanded to new cities across the United States. Micromobility is defined as a category of transportation services that are shared-use, lightweight, and personal use such as electric scooters (escooters), shared bicycles, and electric bicycles (e-bikes). Micromobility vehicles can be person powered, electrically powered, or a combination of the two (CRCOG, 2022; BTS, 2022). One form of micromobility that is gaining popularity is known as bicycle share. In 2020, the North American Bikeshare and Scootershare Association (NABSA) 2020 State of the Industry Report found that an estimated 83.4 million trips were taken in North America alone (Urbanism Next, 2020). Bicycle share is a type of short term vehicle rental service used in cities across the world. The service typically allows users to rent bicycles through a mobile phone app or a kiosk. Users can ride bikes throughout a bike share system's operating area, which is often contained to select, defined locations such as a city’s limits. There are two major types of bike share in the world. The first is docked, which requires docking stations to charge and store the bikes. In this system, a user can pick up a bike at any station and ride and drop it off at any other empty dock station within the system’s network. The second is dockless, which does not require a docking station, and can be parked anywhere. Recently, it has become standard and more affordable for bike share programs to use both shared bikes and scooters as a hybrid or mixed fleet.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".