Analyzing the Behavior and Growth of Cycling in Four North American Cities Before, During, and After the COVID-19 Pandemic
Bibliographic record
Abstract
The paper highlights the changes in cycling patterns and ridership trends across 12 years (including the COVID-19 pandemic) in Montreal, Vancouver, Ottawa, and New York. Using data from 17 bicycle counting stations, changes in the dynamics of daily and weekly profiles before and during pandemic were determined. Additionally, the ridership demand evolution across the years was explored using models that controlled for variations in the weather. All the studied bicycle facilities experienced changes in the daily and hourly patterns in 2020 (the first year of the pandemic), tending toward recreational purposes. Significant growth in bicycle activity during the first year of the pandemic has been found, but trends for the following years (2021 and 2022) have not been studied. This study found that all counting sites located on cycling facilities primarily used for utilitarian purposes experienced a growth in ridership during 2020. Ridership on utilitarian corridors in Montreal and New York City grew considerably during the pandemic before stabilizing in 2021 and 2022. The same counting sites rapidly reverted to utilitarian hourly and daily patterns in 2021. The mixed-utilitarian bicycle facilities in Ottawa and Montreal shifted toward more recreational uses during the pandemic, though ridership did not grow in 2021 and 2022. All the counting sites in Vancouver shifted toward mixed use during the first year of the pandemic and did not show any clear signs of reverting to their utilitarian patterns.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".