Estimating the shift from bicycle to metro in Tianjin
Bibliographic record
Abstract
Seven cities in China, including Tianjin, are building metro systems. The cities vary in structure and transport characteristics, with Tianjin hosting the highest levels of non-motorised transport, in particular bicycles. It is widely expected that some proportion of present journeys involving the bicycle in these cities will be converted to trips including a metro component. This study tested that proposition by surveying over 700 individuals in Tianjin about their most recent trip from home and other daily trips, before the first metro line opened. The study focused on travel time economies by alternative itineraries for their reported trip involving foot and metro, metro and bus, and metro and bicycle combinations. Travel times were obtained from respondents and validated with an electronic trip planner. Time savings of over 15 minutes using the metro for at least part of the journey were possible for 15 per cent of the reported trips. Regression analysis revealed household size, available bicycles and trip distance as significant variables in the choice of the bicycle mode. Modal shift was estimated to be low at this early stage of the metro development, confirmed in metro patronage following the inauguration of metro service. These results prompt questions concerning the future role of the metro in Tianjin. In general, over all the metro cities, the implementation of the metro system could benefit from more customised adaptations of a single metro model to the particularities of the local transport system.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".