The Public Bicycle as a Feeder Mode for Metro Commuters in the Megacity Beijing: Travel Behavior, Route Environment, and Socioeconomic Factors
Bibliographic record
Abstract
The last mile between metro stations and commuters’ homes or workplaces has become one of key topics in relation to metro ridership in megacities where more than 10 million people live. Although the public bicycle is widely believed to be one of new ways to solve the last-mile problem, the determinants of the use of public bicycles by commuters as a feeder mode have been scarcely discussed in the literature. This paper aims to contribute to this topic by using Beijing as a case study. Based on a travel survey among public bike users in Beijing, this study applied the binary logit model and cluster analysis to explore the determinants of public bike use around metro stations. The paper focused on three independent variables: route environment, socioeconomic factors, and travel distance. The results showed that middle-aged and medium-income commuters are more likely to use public bicycles as a feeder mode for metro transport. The built environment had significant effects on public bike use. Most of the cyclists preferred cycling routes with high directness, while high-income and high-education cyclists viewed comfort and safety of the trip as priority factors. Most trips were within 2 km, and a longer travel distance was significantly related to a higher possibility of public bicycle use. The findings and conclusions can enhance our existing understanding of public bike use as a feeder mode to solve the last mile problem and provide new evidence for policymaking on promoting public bikes in megacities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".