Cyclist-Pedestrian Cohabitation in Seasonal Pedestrian Streets
Bibliographic record
Abstract
ABSTRACT: There is a renewed focus on active modes of transportation given their multiple advantages, whether for human health or the environment in general. Interest has grown especially in 2020 after the COVID-19 pandemic, when several cities quickly implemented temporary facilities for walking and cycling in the context of physical distancing. Several measures piggybacked on existing programs such as the Montreal initiative for complete streets ("mes conviviales" or "social/festive streets") that selects streets each year for pilot projects and a final design implementation over a three-year period. This resulted in seasonal pedestrianization of about ten streets each year since 2020. Though active transportation brings together pedestrians and cyclists under a large umbrella, these users have very different characteristics and there may be conflicts of use if mixed in the same space. Cycling is thus generally forbidden on pedestrian streets. Despite these rules, there is cycling traffic on pedestrian streets as cyclists also enjoy car-free facilities, especially when pedestrian traffic is low, which generates complaints by pedestrians. To reconcile and help both categories of users coexist, two Montreal boroughs tried a new rule in the Summer of 2021, to let cyclists bike at walking speed on pedestrian streets while avoiding conflicts with pedestrians. There are few studies on cyclist-pedestrian interactions, and, to the best of the authors' knowledge, none on interactions in pedestrian streets. This work aims to study the coexistence or cohabitation of pedestrians and cyclists in several pedestrian streets through video-based analysis. Data were collected at several sites and on several days during the Summer of 2021 along three different pedestrian streets, two of them allowing cycling, to assess how cyclists and pedestrians interact, whether cycling is allowed or not.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".