Will Peak Travel Observed in Northern Cities Occur in the South ? Case studies in France and Mexico
Bibliographic record
Abstract
In most developed countries urban mobility and car traffic have stagnated since the early 2000s. In France, various data sources show that the trend can be attributed primarily to people living in large urban areas: trips have become less frequent (with unbroken workdays) and less exclusively taken by car (as more young adults adopt multimodal behaviours), and car ownership is decreasing in denser areas, and to a lesser extent, in suburbs. Does this levelling-off of traffic suggest that the saturation point is near? Is this a structural phenomenon (population ageing, etc.) or a cyclical one linked to rising and volatile fuel prices and the recession? We shall explore these issues in the light of data collected in the Urban Areas of Montreal and Lille, and then move on to a comparison with two Mexican cities, Ciudad Juarez on the northern border of Mexico where the level of motorization is high compared to Puebla, our second Mexican case study, where motorization is still low. To each city we apply demographic based projections models in order to consider the extent to which, and in what timeframe, the trends observed in developed cities could spread southward to the emerging economies.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".