Research on Accessibility and Equity of Urban Transport Based on Multisource Big Data
Bibliographic record
Abstract
Daily travel is an important means for everyone to obtain the right to development. With the development of the economy and the progress of the times, the equalization of public infrastructure has become an important concern. The accessibility and fairness of transportation have become a hot topic of research in various fields. To promote transport equity and formulate more reasonable transport planning and policies, this paper takes the Kunshan city as the research object, based on the mobile phone signaling data and the travel time consumption data from the application programming interface (API) of Gaode Map, using weighted average accessibility and the Theil index to investigate the accessibility and equity of public transport and car traffic in the Kunshan city. The study found that the accessibility of public transport is lower than that of car transport in the same research unit, but the equity of public transport is better than that of car transport, that is, the public transport is fair and the efficiency is neglected. In the same mode of transportation, equity presents a high four-week low distribution in the central urban area, and the spatial equity difference is mainly caused by the difference in accessibility levels between cell units. According to the research conclusions, it is recommended that Kunshan further optimize the spatial layout of public transportation infrastructure and adopt measures such as bus speed increase to achieve equity and efficiency and improve the competitiveness of public transportation.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".