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

Date created: 
2020-04-14
Identifier: 
etd20810
Keywords: 
Bicycling
Safety data
Crowdsource
Exposure
Study design
Abstract: 

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.

Document type: 
Thesis
Rights: 
This thesis may be printed or downloaded for non-commercial research and scholarly purposes. Copyright remains with the author.
File(s): 
Senior supervisor: 
Meghan Winters
Department: 
Health Sciences: Faculty of Health Sciences
Thesis type: 
(Thesis) Ph.D.
Statistics: