Toward Safer Roads: Predicting the Severity of Traffic Accidents in Montreal Using Machine Learning

Abstract

Traffic accidents are among the most common causes of death worldwide. According to statistics from the World Health Organization (WHO), 50 million people are involved in traffic accidents every year. Canada, particularly Montreal, is not immune to this problem. Data from the Société de l’Assurance Automobile du Québec (SAAQ) show that there were 392 deaths on Québec roads in 2022, 38 of them related to the city of Montreal. This value represents an increase of 29.3% for the city of Montreal compared with the average for the years 2017 to 2021. In this context, it is important to take concrete measures to improve traffic safety in the city of Montreal. In this article, we present a web-based solution based on machine learning that predicts the severity of traffic accidents in Montreal. This solution uses a dataset of traffic accidents that occurred in Montreal between 2012 and 2021. By predicting the severity of accidents, our approach aims to identify key factors that influence whether an accident is serious or not. Understanding these factors can help authorities implement targeted interventions to prevent severe accidents and allocate resources more effectively during emergency responses. Classification algorithms such as eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Random Forest (RF), and Gradient Boosting (GB) were used to develop the prediction model. Performance metrics such as precision, recall, F1 score, and accuracy were used to evaluate the prediction model. The performance analysis shows an excellent accuracy of 96% for the prediction model based on the XGBoost classifier. The other models (CatBoost, RF, GB) achieved 95%, 93%, and 89% accuracy, respectively. The prediction model based on the XGBoost classifier was deployed using a client–server web application managed by Swagger-UI, Angular, and the Flask Python framework. This study makes significant contributions to the field by employing an ensemble of supervised machine learning algorithms, achieving a high prediction accuracy, and developing a real-time prediction web application. This application enables quicker and more effective responses from emergency services, potentially reducing the impact of severe accidents and improving overall traffic safety.