Prediction of Fatal and Major Injury of Drivers, Cyclists, and Pedestrians in Collisions

Abstract

Traffic-related deaths and severe injuries may affect every person on the roads, whether driving, cycling or walking. Toronto, the largest city in Canada and the fourth largest in North America, aims to eliminate traffic-related fatalities and serious injuries on city streets. The aim of this study is to build a prediction model using data analytics and machine learning techniques that learn from past patterns, providing additional data-driven decision support for strategic planning. A detailed exploratory analysis is presented, investigating the relationship between the variables and factors affecting collisions in Toronto. A learning-based model is proposed to predict the fatalities and severe injuries in traffic collisions through a comparison of two predictive models: Lasso Regression and Random Forest. Exploratory data analysis results reveal both spatio-temporal and behavioural patterns such as the prevalence of collisions in intersections, in the spring and summer and aggressive driving and inattentive behaviours in drivers. The prediction results show that the best predictor of injury severity for drivers, cyclists and pedestrians is Random Forest with an accuracy of 0.80, 0.89, and 0.80, respectively. The proposed methods demonstrate the effectiveness of machine learning application to traffic and collision data, both for exploratory and predictive analytics.

Towards a Sustainable and Safe Future: Mapping Bike Accidents in Urbanized Context

“…Land use also significantly influences cyclists' safety; bicycle accidents are more likely to happen in areas with a high population density, a variety of land uses, and lots of pedestrian traffic [13,14]. Furthermore, areas with high concentrations of cyclists and motorists can result in conflicts that lead to accidents [15,16]. Considering these threats when planning cities and neighborhoods helps to lessen the negative impact of cycling.…”

Prediction of Fatalities in Vehicle Collisions in Canada

“…In Canada, important research is conducted on severe collisions examining its causes and impacts. The research in [15] analysed all traffic collision events in which at least one person was killed or seriously injured in Toronto. The spatio-temporal and behavioural patterns are examined using the Killed or Seriously Injured dataset covering the years from 2007 to 2017.…”