Urban transport patterns in a global sample of cities & their linkages to transport infrastructure, land use, economics & environment
Bibliographic record
Abstract
Urban transport and the issue of motorisation or 'automobile dependence' have become critical shaping factors in the future sustainability and livability of all cities. This paper provides an overview of a selected group of factors that help define some of the main features of urban transport in metropolitan regions around the world. The aim is to provide decision-makers and policy analysts some basic perspective on where cities in their region sit in a global context. The paper also points to some key policy issues that emerge from the data and which have considerable bearing on issues such as priorities in urban infrastructure development. The data are drawn from the Millennium Cities Database for Sustainable Transport compiled over 3 years by the authors for the International Association of Public Transport (UITP) in Brussels. The database provides data on 100 cities on all continents. Data summarised here represent regional averages from 84 of these fully completed cities in the USA, Australia and New Zealand, Canada, Western Europe, Asia (high and low income areas), Eastern Europe, The Middle East, Latin America, Africa and China. (A)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| 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) | 0.002 | 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".