Shared Spaces in Canada: an evaluation of their applicability using focus-groups
Bibliographic record
Abstract
Traffic conflicts between pedestrians, cyclists and motorized vehicles are handled with stop signs, traffic lights, pedestrian crossings and bicycle tracks and lanes. If these traffic management measures are still present in most 30km/h or reduced speed zones, they are not part of a shared space type of street, named “zone de rencontre” (ZR). In such a zone, motorized vehicles and vulnerable users share the same infrastructure, and pedestrian have priority over vehicles and can cross anywhere on the designated section. After years of try-outs, this concept was officially accepted in Switzerland (2002) and is now spreading in Europe. It is also raising interest among traffic planners in North America. The Ministry of Transportation of Quebec asked Polytechnique Montreal to evaluate the safety outcomes and the applicability of this concept in a Canadian traffic context, considering all aspects (roadway design, winter maintenance, driving culture and our roadway Code). Since ZR are not yet implemented in Canada, it was decided to present European cases to Canadian experts to judge the applicability of the concept. Experts were consulted through a series of 13 focusgroup held cities of various sizes, gathering just over 223 experts. A special attention was drawn on ZR in a context where a high number of pedestrian interacts with high traffic volumes. Videos and photos from specific cases were shown to experts. Design, environment and traffic conditions were judged for their applicability in a Canadian context. One of the main finding is that the vast majority of experts and officials are willing to introduce, in the Highway Safety Code a “caution principle”, considering that all users must pay attention to other users, especially the most vulnerable ones. Experts also believed that pedestrians and bicyclists should have priority over motorized vehicles. They agreed that ZR could be introduced, but inside a pilot-project frame, since there is comprehensive fear around the concept, especially for pedestrians visually impaired. Linear central refuges for pedestrian, allowing crossing in a two-step sequence, was found an interesting design solution for two-lane roadways, since it reduces speeds and it is forgiving for vulnerable users. Finally, since ZR are not known by the general public, it is necessary to develop an information campaign for all roadway users if such zone are to be implemented.
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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 | 0.045 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".