Analysis of the Relationship between Dockless Bicycle-Sharing and the Metro: Connection, Competition, and Complementation
Bibliographic record
Abstract
Dockless bicycle-sharing (DLBS) is one of the novel transportation modes emerging in recent years. As a newly arisen mode, dockless bicycle-sharing inevitably has influence on the existing components of the public transportation system, especially the metro system. A large number of scholars have explored the integration relationship between the two. However, through the evaluation and quantification of the dockless bicycle-sharing data and the metro automatic fare collection data, we find that the relationship between the two is not unique. Based on the location of origin and destination, the travel duration, and the travel distance, the dockless bicycle-sharing trips closely related to the metro were identified and categorized into three different temporal-spatial relationships: competition trips, connection trips, and complementation trips. Three indicators were proposed to characterize the relationship between the two systems. A case study was carried out in Shanghai, China. The proposed method was applied to investigate when, where, and to what extent the dockless bicycle-sharing trips compete with, integrate with, and complement the metro. The results show that dockless bicycle-sharing mainly integrates with and complements the metro. It is where the dockless bicycle-sharing trip takes place and the trip significantly determines its relationship with the metro. The findings provide significant implications regarding the design and management of dockless bicycle-sharing and the metro.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".