Analyzing the Impact of Montreal’s Réseau Express Vélo (REV) on Surrounding Bike Lanes’ Ridership and the COVID-19 Cycling Recovery
Bibliographic record
Abstract
Amidst the COVID-19 pandemic, Montreal implemented pro-cycling measures and enhanced its cycling infrastructure, notably by the introduction of the Réseau Express Vélo (REV), an extensive network of separated high-capacity bike lanes inaugurated in 2020. This paper delves into the pandemic’s impact on Montreal’s cycling network, with specific focus on the Berri/Lajeunesse/Saint-Denis REV route, evaluating its influence on the recovery of adjacent bike lanes and its effectiveness in attracting new cyclists. Using data from sensors installed along Montreal’s bike lanes between 2018 and 2023, our analysis reveals an initial average 28% decline in cycling volumes at the onset of the pandemic. However, from 2021 onwards, disparities began to emerge. While sensors on adjacent parallel bike routes to REV recorded further declines in ridership, those elsewhere in the city showed signs of cycling recovery, suggesting a shift in the cycling patterns towards the REV. Distinguishing between cyclists displaced from other parallel bike lanes and those representing a pent-up demand, our study indicates that the displaced cyclists accounted for 64% of the REV’s ridership at the southernmost sensors and only 7% at the northernmost sensors in 2023. These findings have significant policy implications, as cyclists comprised within the pent-up demand category correspond to those having transitioned from not using bike lanes to utilizing safer cycling infrastructure and account for the net growth in the cycling mode share directly attributable to the REV. Both of these observations are pivotal in fostering a shift away from cars and steering Montreal towards achieving its cycling mode share goal of 15% by 2027.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".