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.

During the earlier stages of the COVID-19 pandemic, numerous studies used bicycle count data to understand the pandemic's impact on urban cycling. However, given the context and timeliness of such studies, correcting for periods of missing data and ensuring data accuracy were not always possible. In this paper, we examine the quality of bicycle count data, using Montreal as a case study, and propose methodological improvements. By employing a missing value imputation technique and excluding sensors potentially affected by exogenous factors, we hope to provide cities with a more precise assessment of cycling trends.