Sharing the Street in Urban Areas: The Example of the 'Code de la Rue' (Street Use Code) in France
Bibliographic record
Abstract
Street design, when associated with appropriate speed and travel regulations, plays a decisive role in promoting a change in travel behaviour and in making cities more user-friendly . The allocation of space, speed limits for vehicles and user priority rules are also elements that influence the comfort and safety of pedestrian and cyclist circulation and, in turn, the choice of travel mode. Several road-sharing concepts developed in other countries are of great interest; the code de la rue (street use code) in France provides an excellent example. This approach, which was launched in 2006, has led to substantial advances, both in terms of regulations and urban street planning. Cities now have at their disposal a range of concepts that are adapted to different categories of urban roads, characterized by speed management associated with a specific way of sharing the public space. In a context where we seek to promote sustainable mobility and to review how the road is shared, this makes for a very interesting example. These objectives are at the heart of debates in Quebec. The proliferation of initiatives, the wide variety of stakeholders involved and the mechanisms of consultation set in place are very important success factors in overcoming major challenges that transportation and road safety present in urban areas. (A) This paper was translated from a French paper published in the 2012 Transportation Association of Canada Annual Proceedings (see ITRD number 201211RT670F).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".