Impacts of Bicycle Infrastructure in Mid-Sized Cities (IBIMS): protocol for a natural experiment study in three Canadian cities

Introduction: Bicycling is promoted as a transportation and population health strategy globally. Yet bicycling has low uptake in North America (1%–2% of trips) compared with European bicycling cities (15%–40% of trips) and shows marked sex and age trends. Safety concerns due to collisions with motor vehicles are primary barriers.  To attract the broader population to bicycling, many cities are making investments in bicycle infrastructure. These interventions hold promise for improving population health given the potential for increased physical activity and improved safety, but such outcomes have been largely unstudied. In 2016, the City of Victoria, Canada, committed to build a connected network of infrastructure that separates bicycles from motor vehicles, designed to attract people of ‘all ages and abilities’ to bicycling.  This natural experiment study examines the impacts of the City of Victoria’s investment in a bicycle network on active travel and safety outcomes. The specific objectives are to (1) estimate changes in active travel, perceived safety and bicycle safety incidents; (2) analyse spatial inequities in access to bicycle infrastructure and safety incidents; and (3) assess health-related economic benefits.  Methods and analysis: The study is in three Canadian cities (intervention: Victoria; comparison: Kelowna, Halifax). We will administer population-based surveys in 2016, 2018 and 2021 (1000 people/city). The primary outcome is the proportion of people reporting bicycling. Secondary outcomes are perceived safety and bicycle safety incidents. Spatial analyses will compare the distribution of bicycle infrastructure and bicycle safety incidents across neighbourhoods and across time. We will also calculate the economic benefits of bicycling using WHO’s Health Economic Assessment Tool.  Ethics and dissemination: This study received approval from the Simon Fraser University Office of Research Ethics (study no. 2016s0401). Findings will be disseminated via a website, presentations to stakeholders, at academic conferences and through peer-reviewed journal article

Evaluating and utilizing crowdsourced data and population surveys in bicycling safety research

Increased population level bicycling would benefit society by improving health outcomes and reducing fossil fuel emissions. A main factor preventing increased bicycling is concerns regarding safety. Traditional sources of bicycling safety data (police, hospital or insurance data) underreport incidents and are biased. Alternative sources of bicycling safety data, including crowdsourcing and population surveys, are untested and rarely utilized. Crowdsourced data will include incidents that go unreported to traditional sources, but the nature of any systematic biases in these data are poorly understood. Population surveys represent the only means of collecting detailed individual-level information regarding road users, but there is little consideration by researchers of how survey design choices may affect measured outcomes. When combined with spatial data, population surveys can contribute to understanding associations between rarely studied characteristics of road users and perceived or objective safety. In this thesis, I evaluate alternative sources of bicycling safety data, and contribute to different dimensions of bicycling safety knowledge, by evaluating bicycling safety data collection methods and identifying correlates of perceived and objective bicycling safety. Specifically, the chapters in this thesis address gaps in our understanding of (i) biases in crowdsourced bicycling safety data, (ii) the relationship between personal characteristics, infrastructure, and overall perceived bicycling safety, (iii) the impacts of survey design on measurements of bicycling behaviour, and (iv) bicycling crash risk for different sociodemographic characteristics, social environments (including attitudes and social norms), and neighbourhood-built environment features. In this thesis I provide two broad contributions: (i) showcasing the potential for crowdsourced data and population surveys to compliment traditional bicycling safety data and, provide answers to applied question in bicycling safety research; (ii) underscoring the value of linking a-spatial survey data to a geographic location to be able to assign measurements of a participants built environment and, be able to consider different scales of influence on the outcome. Future research in this area should focus on creating a linked crash database of self-report, crowdsourced, police, hospital and insurance data, as well as on the collection and integration of spatially resolved exposure estimates in travel surveys

Translating risk to preventable burden by estimating numbers of bicycling injuries preventable by separated infrastructure on a Toronto, Ontario corridor

Objectives: Bicycling is a form of active transportation with a number of health benefits but carries a high risk of injury compared to other transportation modes. Safety intervention evaluations often produce results in the form of ratios, which can be difficult to communicate to policy-makers. The primary objective of this study was to estimate the number of bicycling injuries on an urban corridor preventable by separated bicycling infrastructure. Methods: Stakeholders identified a key corridor with multiple segments having bicycling infrastructure but most of the corridor lacking similar infrastructure. We counted bicyclist volume along this route and used secondary data to supplement counts missing due to COVID-19. We used two reference studies including local bicycling population to estimate benefit of separated bicycling infrastructure and applied this to a city-wide estimate of baseline risk of injury per kilometre bicycled, which used a combination of secondary data sources including police, health care and travel survey data. Finally, we adjusted baseline risk to account for increased bicyclist volume during and following the COVID-19 pandemic. Results: We estimated installation of fully separated cycle tracks along one Toronto corridor would prevent approximately 152.9 injuries and 0.9 fatalities over a 10-year period. Discussion: Our results underscore the benefits of separated bicycling infrastructure. We identify several caveats for our results, including the limitations of studies used to estimate relative risk of infrastructure. Our method could be adapted for use in other cities or along other corridors. Finally, we discuss the role of preventable burden estimates as a knowledge translation tool

Estimating walking and bicycling in Canada and their road collision fatality risks: The need for a national household travel survey

Canada does not conduct a national household travel survey, resulting in a data gap on walking and bicycling. These data are key to surveillance of physical activity and health, as well as in epidemiological injury risk calculations. This study explored the use of available national data sources, the Canadian census and the Canadian Community Health Survey (CCHS), to tally walking and bicycling and examine trends in fatality risk. Estimates of the percentage and number of Canadians walking or bicycling to work were calculated for 1996–2016 using the census. The CCHS was used to estimate the number and proportion of Canadians walking or bicycling for leisure (2000–2014) and to work or school (2008–2014). We combine these data with National Collision Database data on the number of pedestrian and bicyclist fatalities (1999–2017) and compare trends in fatality risk over time using each dataset. Across all data sources, walking was more common among women, while bicycling was more common among men. Men were at higher fatality risk than women. These results should be interpreted with caution given limitations this study identifies in census and CCHS data, including narrow definitions for bicycling behaviour, lack of detail regarding amount of use, and inconsistency of questions asked over time. A national household travel survey should be a priority for public health purposes in Canada

Impacts of study design on sample size, participation bias, and outcome measurement: A case study from bicycling research

Introduction Measuring bicycling behaviour is critical to bicycling research. A common study design question is whether to measure bicycling behaviour once (cross-sectional) or multiple times (longitudinal). The Physical Activity through Sustainable Transport Approaches (PASTA) project is a longitudinal cohort study of over 10,000 participants from seven European cities over two years. We used PASTA data as a case study to investigate how measuring once or multiple times impacted three factors: a) sample size b) participation bias and c) accuracy of bicycling behaviour estimates. Methods We compared two scenarios: i) as if only the baseline data were collected (cross-sectional approach) and ii) as if the baseline plus repeat follow-ups were collected (longitudinal approach). We compared each approach in terms of differences in sample size, distribution of sociodemographic characteristics, and bicycling behaviour. In the cross-sectional approach, we measured participants long-term bicycling behaviour by asking for recall of typical weekly habits, while in the longitudinal approach we measured by taking the average of bicycling reported for each 7-day period. Results Relative to longitudinal, the cross-sectional approach provided a larger sample size and slightly better representation of certain sociodemographic groups, with worse estimates of long-term bicycling behaviour. The longitudinal approach suffered from participation bias, especially the drop-out of more frequent bicyclists. The cross-sectional approach under-estimated the proportion of the population that bicycled, as it captured ‘typical’ behaviour rather than 7-day recall. The magnitude and directionality of the difference between typical weekly (cross-sectional approach) and the average 7-day recall (longitudinal approach) varied depending on how much bicycling was initially reported. Conclusions In our case study we found that measuring bicycling once, resulted in a larger sample with better representation of sociodemographic groups, but different estimates of long-term bicycling behaviour. Passive detection of bicycling through mobile apps could be a solution to the identified issues

Cyclist crash rates and risk factors in a prospective cohort in seven European cities

Increased cycling uptake can improve population health, but barriers include real and perceived risks. Crash risk factors are important to understand in order to improve safety and increase cycling uptake. Many studies of cycling crash risk are based on combining diverse sources of crash and exposure data, such as police databases (crashes) and travel surveys (exposure), based on shared geography and time. When conflating crash and exposure data from different sources, the risk factors that can be quantified are only those variables common to both datasets, which tend to be limited to geography (e.g. countries, provinces, municipalities) and a few general road user characteristics (e.g. gender and age strata). The Physical Activity through Sustainable Transport Approaches (PASTA) project was a prospective cohort study that collected both crash and exposure data from seven European cities (Antwerp, Barcelona, London, Örebro, Rome, Vienna and Zürich). The goal of this research was to use data from the PASTA project to quantify exposure-adjusted crash rates and model adjusted crash risk factors, including detailed sociodemographic characteristics, attitudes about transportation, neighbourhood built environment features and location by city. We used negative binomial regression to model the influence of risk factors independent of exposure. Of the 4,180 cyclists, 10.2 % reported 535 crashes. We found that overall crash rates were 6.7 times higher in London, the city with the highest crash rate, relative to Örebro, the city with the lowest rate. Differences in overall crash rates between cities are driven largely by crashes that did not require medical treatment and that involved motor-vehicles. In a parsimonious crash risk model, we found higher crash risks for less frequent cyclists, men, those who perceive cycling to not be well regarded in their neighbourhood, and those who live in areas of very high building density. Longitudinal collection of crash and exposure data can provide important insights into individual differences in crash risk. Substantial differences in crash risks between cities, neighbourhoods and population groups suggest there is great potential for improvement in cycling safety