Study on the Spatiotemporal Characteristics of Bike‐Sharing and Urban Public Transport Integration: A Case Study of Lanzhou, China
Bibliographic record
Abstract
With urbanization, public transportation resources are becoming increasingly strained. As a key complement to urban transit systems, shared bikes offer distinct advantages in solving the ‘last‐mile’ issue for urban commuters. However, one pressing challenge in integrating shared bikes with public transportation is the uneven spatiotemporal distribution. Using Lanzhou City as a case study, this paper provides a detailed analysis of the spatiotemporal characteristics of shared bike and public transport connections. Through the mining of cycling data and analysis of travel demands, a random forest regression (RFR) model is employed to identify factors influencing shared bike usage. The results reveal that variables such as age, population density, and cycling distance significantly impact the efficiency of shared bike connections. Based on these findings, several improvement strategies are proposed, including optimizing the allocation and distribution of shared bikes, addressing the specific needs of various age groups, enhancing cycling safety, and improving bike maintenance. By implementing these strategies, the integration of shared bikes with public transport can be enhanced, increasing shared bike usage and improving the overall efficiency of urban commuting, while promoting green travel and sustainable urban development.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".