Background Motor-vehicle collisions (MVCs) are a leading cause of Canadian child pedestrian/ bicyclist injuries. Fears of MVCs can further reduce active transportation prevalence, and the accompanying health benefits. Built environment features (e.g., curb extensions) may reduce child injury risk at specific locations; however, it is important to understand how these features can impact community wide child active transportation safety outcomes.~Objective This study seeks to develop a comprehensive machine/statistical learning model capable of predicting the risk of child bicyclist/pedestrian-MVCs and active transportation prevalence at the community level, based on the built environment features within various Canadian municipalities.~Design Investigators will construct a geodatabase to be used to train and test multiple machine learning models(1). Given that child Motor-Vehicle Collisions (MVCs) do not occur randomly, but like other diseases are distributed according to the influencing characteristics, these models may be capable of establishing associations between the built environment, child active transportation prevalence, and child injury risk and fatality in specific Canadian municipalities.~Data Data for this project has been obtained through the CHild Active-Transportation Safety and the Environment (CHASE) program(2). Using geographic information system (GIS)(3,4) software (i.e., ArcGIS), CHASE data and its accompanying spatial characteristics have been consolidated into a comprehensive geodatabase allowing for managing and storing spatially characterized data. This database contains over 300 variables and over 200 layers of interest characterized by the 3Ds of the built environment(5). This includes density (e.g., population demographics, city boundaries, age, social economic status), diversity (e.g., school attendance, student bussing numbers, crossing guards, housing land use, parks), and design features (e.g., built environment features, road network, traffic calming, bicycle infrastructure, traffic volume/speed). This database also includes child active transportation (e.g., mode share, prevalence), and injury (e.g., MVC locations, police collision reports) outcomes.~These data have been obtained by the CHASE team from national, provincial, and municipal sources, including Alberta Transportation, Stats Canada, the Toronto District School Board, the City of Calgary, Eco-Counter, and specific CHASE projects(2). CHASE projects included in-person audits of the built environment surrounding elementary schools, direct observation of elementary students/parents mode of transportation to school, quasi-experimental studies of specific built environment interventions, and comparisons of the built environment between locations of a child bicyclist injury and control locations. Both this project and the CHASE program focuses on the municipalities of Vancouver, Surrey, Calgary, Toronto, Peel, Montreal, and Laval; with data collected for each year between 2010 and 2020. As such, the database used in this study will contain over 8,000 child (age 1-17) bicyclist and pedestrian injuries, and active transportation prevalence at over 500 elementary schools.~Model Development When investigating traffic safety, many different machine learning models can be used including Bayesian neural networks, support vector machines, random forests, and nearest neighbour classification(6 -9). To ensure reproducibility of results(1), this study will test and compare multiple supervised machine learning algorithms.~After preparing the CHASE geodatabase, the data will be split into training and evaluation sets. The training set will include a random sample of 80% of the data from each municipality, with the remainder used in the evaluation set. Splitting the data by municipality rather than the entire dataset, ensures that the final model can be tested against the widest range of urban designs possible.~Initially, all available variables will be used in developing the model. To ensure a parsimonious model, various strategies (e.g., backward elimination, Bayesian Information Criterion) will be used to retain only those variables that have an influence on the outcome, or those that may have a confounding effect on other important variables(10). Evaluations of collinearity of similar predictor variables will also be conducted throughout development (e.g., Principle Component Analysis), with subsequent adjustment of models. During training, k-fold cross-validation will be used for resampling the training data.~Outcomes Child pedestrian/bicyclist injury and active transportation outcomes, along with their predictor variables, will be analyzed according to community boundary buffer zones within each municipality. This will allow investigation into the density of specific built environment features per community boundary, and its effect on child active transportation injury risk and active transportation prevalence. Comparison of the accuracy of the various algorithms will lead to a chosen model able to determine whether specific covariates increase or decrease child pedestrian/bicyclist injury risk or active transportation prevalence.
This study seeks to develop a comprehensive machine/statistical learning model capable of predicting the risk of child bicyclist/pedestrian-MVCs and active transportation prevalence at the community level, based on the built environment features within various Canadian municipalities.~Investigators will construct a geodatabase to be used to train and test multiple machine learning models(1). Given that child Motor-Vehicle Collisions (MVCs) do not occur randomly, but like other diseases are distributed according to the influencing characteristics, these models may be capable of establishing associations between the built environment, child active transportation prevalence, and child injury risk and fatality in specific Canadian municipalities.