Triangulating conventional and crowdsourced data to evaluate how investments in safe and connected bicycle facilities impact ridership and gender equity

Keywords: bicycling, crowdsourced data, strava, equity, intervention research, urban planning,
Abstract Type: Paper Abstract

Abstract

Governments are investing in active transportation infrastructure to support healthy, resilient city building. An example is the City of Victoria, BC, Canada, who invested $11+ million to build a connected network of ‘All Ages and Abilities’ (AAA) bicycling facilities. Longitudinal studies of such interventions are limited by a lack of spatially-temporally continuous ridership volumes, and the rarity of disaggregated data hampers evaluation of whether these interventions increase ridership in populations that are currently underrepresented in bicycling, such as women. Our study aims to address these gaps using conventional and crowdsourced data on bicycle ridership. We are integrating observational counts and crowdsourced data from Strava, a popular mobile fitness app to quantify change in ridership (overall and for women specifically) pre – post intervention (2016 – 2022) and evaluate the use of crowdsourced data for monitoring change in bicycling over time. We are modeling ridership change using hierarchical linear models and assessing spatial patterns of change using a local indicator of spatial autocorrelation. Early results from observational counts indicate that overall ridership on the network increased more than in areas without investment, and women’s ridership also increased.Though a positive trend overall, ridership through COVID-19 restrictions (2020 – 2021) was lower on the network, which may have been designed to support commute trips more than recreation. At the conference we will detail how observational and crowdsourced data compare for tracking change over time and consider how bicycling policy might further gender equity by prioritizing routes that enable care-related and other non-commute trips.