Transforming Commercial Arterials into Bicycle Highways: Using Count Data
Bibliographic record
Abstract
Count data for cyclists and pedestrians is considered an essential tool for city builders to inform, justify and manage active transportation infrastructure. When automated bike counters are strategically deployed across an urban area, a clear picture emerges of how cyclists move around the city. This presentation focuses on how count data can be used to illustrate modal shifts in response to improvements made to a city’s bicycle network. In November 2020, the City of Montreal introduced the Reseau Express Velo (REV), or Express Bike Network, effectively transforming a network of arterials throughout the city into Complete Streets by repurposing vehicles lanes into dedicated AAA bicycle pathways. Using data from automated bicycle counters installed throughout the city, it was found that the REV bicycle paths became the most highly used facility in the entire city within six months of installation. Using count data from automated pedestrian and bicycle Eco-counters, as well as open vehicle count data from the City of Montreal, both collected before and after the REV was deployed on Saint-Denis Street, Eco-Counter performed a study to reveal new transport habits and assess modal shift. Sales data collected by the City of Montreal was also used to support the changes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".