A Street-Specific Analysis of Level of Traffic Stress Trends in Strava Bicycle Ridership and its Implications for Low-Stress Bicycling Routes in Toronto

This study uses Strava bicycling data to investigate network level patterns of bicycle ridership in Toronto, Canada based on Level of Traffic Stress (LTS). We found that most bicycling occurred on a small fraction of the network, with just 10% of all roads and paths accounting for 75% of all bicycle kilometres travelled in 2022. Low-stress routes (LTS 1 and LTS 2) were more popular than high-stress routes for the top 80% most popular streets. The majority of bicycle kilometres travelled (84%) in LTS 2 occurred on routes with no bicycle infrastructure, highlighting the importance of quiet residential streets in forming a low-stress bike network. Despite high-stress conditions, some LTS 3 and LTS 4 streets were heavily used, suggesting infrastructure gaps in Toronto's bicycle network

Who Is Ready to Bicycle? Categorizing and Mapping Bicyclists with Behavior Change Concepts

Bicyclist categorizations have been developed to sort individuals into distinct groups based on shared traits, which can help researchers and practitioners understand complex patterns of bicycling behavior. Previous categorizations have focused on bicycle facility comfort, seasonal patterns of use, and behaviors and attitudes, but not on readiness for bicycling. We present the added-value of a categorization of bicyclists based on the stages of change feature of the Transtheoretical Model (TTM) and examine how this new categorization can contribute unique insights for practice through novel behavioral information and findings from mapping and spatial analysis. We use survey data from a sample of 2398 individuals from three medium-sized Canadian cities: Victoria and Kelowna in British Columbia, and Halifax, Nova Scotia. We categorize individuals into the five TTM stages of change according to three questions: intent to bicycle more, use of a bicycle in the past 12 months, and whether or not they usually use a bicycle to get around. One-third of respondents had not considered bicycling (Pre-contemplation) while one-fifth had begun considering or preparing to bicycle (Contemplation and Preparation) and two-fifths occasionally bicycled (Action). Only 5% regularly bicycled (Maintenance). Men, younger individuals, and residents of Kelowna and Victoria (compared to Halifax) were more likely to be in advanced readiness stages (Action and Maintenance). We used spatial statistical techniques to locate hotspots where there were disproportionately more Action-stage individuals as these could be areas where infrastructure improvements would likely be met with the greatest increase in bicycling; however, results suggested Action-stage individuals were dispersed geographically. We show that categorizing people as a function of readiness for change allows for characterization of populations that are likely to be beneficially impacted by policies to support bicycling. By focusing on readiness to bicycle, this approach could be used by practitioners to prioritize bicycling interventions

Generalized model for mapping bicycle ridership with crowdsourced data

This work was supported by a grant (#1516-HQ-000064) from the Public Health Agency of Canada. MW is supported by a Scholar Award from the Michael Smith Foundation for Health Research.Fitness apps, such as Strava, are a growing source of data for mapping bicycling ridership, due to large samples and high resolution. To overcome bias introduced by data generated from only fitness app users, researchers build statistical models that predict total bicycling by integrating Strava data with official counts and geographic data. However, studies conducted on single cities provide limited insight on best practices for modeling bicycling with Strava as generalizability is difficult to assess. Our goal is to develop a generalized approach to modeling bicycling ridership using Strava data which enables detailed mapping that is more inclusive of all bicyclists and will support better and more equitable decision-making across cities. We used Strava data, official counts, and geographic data to model Average Annual Daily Bicycling (AADB) in five cities: Boulder, Ottawa, Phoenix, San Francisco, and Victoria. Using a machine learning approach, LASSO, we identify variables important for predicting ridership in all cities, and independently in each city. Using the LASSO-selected variables as predictors in Poisson regression, we built generalized and city-specific models and compared accuracy. Our results indicate generalized prediction of bicycling ridership on a road segment in concert with Strava data should include the following variables: number of Strava riders, percentage of Strava trips categorized as commuting, bicycling safety, and income. Inclusion of city-specific variables increased model performance, as the R2 for generalized and city-specific models ranged from 0.08–0.80 and 0.68–0.92, respectively. However, model accuracy was influenced most by the official count data used for model training. For best results, official count data should capture diverse street conditions, including low ridership areas. Counts collected continuously over a long time period, rather than at peak periods, may also improve modeling. Modeling bicycling from Strava and geographic data enablesmapping of bicycling ridership that is more inclusive of all bicyclists and better able to support decision-making.Publisher PDFPeer reviewe

Generalized model for mapping bicycle ridership with crowdsourced data

Fitness apps, such as Strava, are a growing source of data for mapping bicyclingridership, due to large samples and high resolution. To overcome bias introduced by data generated from only fitness app users, researchers build statistical models that predict total bicycling by integrating Strava data with official counts and geographic data. However, studies conducted on single cities provide limited insight on best practices for modeling bicycling with Strava as generalizability is difficult to assess. Our goal is to develop a generalized approach to modeling bicycling ridership using Strava data which enables detailed mapping that is more inclusive of all bicyclists and will support better and more equitable decision-making across cities. We used Strava data, official counts, and geographic data to model Average Annual Daily Bicycling (AADB) in five cities: Boulder, Ottawa, Phoenix, San Francisco, and Victoria. Using a machine learning approach, LASSO, we identify variables important for predicting ridership in all cities, and independently in each city. Using the LASSO-selected variables as predictors in Poisson regression, we built generalized and city-specific models andcompared accuracy. Our results indicate generalized prediction of bicycling ridership on a road segment in concert with Strava data should include the following variables: number of Strava riders, percentage of Strava trips categorized as commuting, bicycling safety, and income. Inclusion of city-specific variables increased model performance, as the R2 for generalized and city-specific models ranged from 0.08–0.80 and 0.68–0.92, respectively. However, model accuracy was influenced most by the official count data used for model training. For best results, official count data should capture diverse street conditions, including low ridership areas. Counts collected continuously over a long time period, rather than at peak periods, may also improve modeling. Modeling bicycling from Strava and geographic data enablesmapping of bicycling ridership that is more inclusive of all bicyclists and better able tosupport decision-making

Understanding the Associations among Social Vulnerabilities, Indigenous Peoples, and COVID-19 Cases within Canadian Health Regions

Indigenous Peoples are at an increased risk for infectious disease, including COVID-19, due to the historically embedded deleterious social determinants of health. Furthermore, structural limitations in Canadian federal government data contribute to the lack of comparative rates of COVID-19 between Indigenous and non-Indigenous people. To make visible Indigenous Peoples’ experiences in the public health discourse in the midst of COVID-19, this paper aims to answer the following interrelated research questions: (1) What are the associations of key social determinants of health and COVID-19 cases among Canadian health regions? and (2) How do these relationships relate to Indigenous communities? As both proximal and distal social determinants of health conjointly contribute to COVID-19 impacts on Indigenous health, this study used a unique dataset assembled from multiple sources to examine the associations among key social determinants of health characteristics and health with a focus on Indigenous Peoples. We highlight key social vulnerabilities that stem from systemic racism and that place Indigenous populations at increased risk for COVID-19. Many Indigenous health issues are rooted in the historical impacts of colonization, and partially invisible due to systemic federal underfunding in Indigenous communities. The Canadian government must invest in collecting accurate, reliable, and disaggregated data on COVID-19 case counts for Indigenous Peoples, as well as in improving Indigenous community infrastructure and services