Identifying metro station types based on transfer purposes: An application of bike‐sharing data in Xiamen, China
Bibliographic record
Abstract
Abstract With the networking of urban rail transit and the large‐scale development of bike‐sharing, metro and bike‐sharing connection has become the preferred way of daily travel for residents of Xiamen. Current studies mainly identify metro station types based on node and place orientation, lacking behaviour‐based investigation. To fill this gap, this study aims to explore the classification of metro stations based on transfer purposes by combining bike‐sharing and point of interest data in Xiamen, using buffer analysis, kernel density estimation, and DBSCAN clustering algorithm comprehensively. The results indicate the following. (1) Distinct transfer purposes have significant agglomeration characteristics and present poly‐centric spatial pattern, an authentic portrayal of Xiamen's land use function. (2) The heterogeneity of connection flow between different transfer purposes and metro stations is apparent. The distribution of flow and flow direction within the same transfer purpose is also in non‐equilibrium. (3) Based on traffic connection analysis, metro stations are divided into seven types: transportation hub, employment‐oriented, residence‐oriented, job‐housing balance, school‐oriented, traffic‐tourism integration, and business connection types. The obtained results assist in improving the transportation connection environment, perfecting urban land use planning, and enhancing low‐carbon and green travel.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".