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.
Highlights
The World Health Organization estimates that over 3,400 people die in traffic collisions on a daily basis, and tens of millions are injured and disabled on a yearly basis [1]
Collisions are studied through various angles, such as the development of Accident Prediction Models (APM), road safety measure assessment, user behaviour analysis and others [4]
The goal in this study is to identify the patterns in Toronto severe and fatal collisions and to build a predictive model to estimate injury severity of Promet – Traffic&Transportation, Vol 32, 2020, No 1, 39-53
Summary
The World Health Organization estimates that over 3,400 people die in traffic collisions on a daily basis, and tens of millions are injured and disabled on a yearly basis [1]. Collisions are studied through various angles, such as the development of Accident Prediction Models (APM), road safety measure assessment, user behaviour analysis and others [4]. Outside Canada, many studies analyse the physical aspect of a collision, such as structure, weight, and velocity of a car with regards to cyclists’ [11] and pedestrians’ injuries [12]. Both studies proposed safety measures to dampen the severity of injuries resulting from such collisions. Studies on the drivers' behaviour and personality traits reveal that impulsivity and aggressiveness, as well as driver fatigue, are significant contributors to traffic collision occurrence [16, 17], and may lead to severe injuries [18]
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