Understanding the Spatial Variation of Integrated Use of Ride‐Hailing Services With the Metro
Bibliographic record
Abstract
This study aims to explore the spatial heterogeneity of influential factors of integrated use of ride‐hailing service with the metro. Using the operation data of ride‐hailing services in Chengdu, China, first, an identification method of integrated ride‐hailing trips is proposed. Then, the ordinary least squares (OLS) and geographically weighted regression (GWR) models are established to discern the factors that affect access‐integrated ride‐hailing use and egress‐integrated ride‐hailing use on weekdays and weekends. The model results demonstrate that the fitting effect of GWR models is superior to that of OLS models, and the coefficient estimates of each explanatory variable vary across regions. Accommodation facilities promote the access‐integrated trips in the eastern area, and this positive impact for egress‐integrated trips extends to the northeastern area. Tourist attractions have a positive impact on the integrated trips in the central and western regions, while they have a negative impact in the northwest and southeast regions. The research results can provide the theoretical support for the seamless connection and coordinated development of these two services.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".