Evaluation of Dockless Bike-Sharing Transfer Services around Metro Stations considering Spatial Heterogeneity
Bibliographic record
Abstract
The dockless bike-sharing system provides a convenient transfer option for metros, which can solve the “last mile” problem for passengers. However, the quality of bike-sharing transfer services around metro stations and its influencing factors are still unrevealed. Therefore, this study aims to evaluate the bike-sharing transfer services for different metro stations considering spatial heterogeneity. First, the transfer ridership of bike-sharing around each metro station is calculated based on the trip data of the Mobike in Shanghai. Then, a geographically weighted regression (GWR) model is used to analyze the relationships between the transfer ridership and built environment features around each metro station. Given the GWR model, a spatially varying benchmark is established to independently evaluate the bike-sharing transfer service of each metro station based on the deviation between the actual transfer ridership and its benchmark. In this case, the metro stations whose actual bike-sharing transfer ridership is below the benchmark are identified as the objects that need to improve their bike-sharing transfer services. The results show that the benchmark derived from the GWR model varies from station to station depending on the metro ridership, population density, and cycleway density around the metro station. In addition, bike-sharing transfer services around metro stations are better in Xuhui District, Minhang District, and Huangpu District, and worse in Pudong District and Qingpu District. Therefore, this study is helpful for the government and operators to better optimize the connection between dockless bike-sharing and metros.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".