Version 1
: Received: 12 May 2024 / Approved: 13 May 2024 / Online: 13 May 2024 (08:28:03 CEST)
How to cite:
Muktar, B.; Fono, V. Towards Safer Roads: Predicting the Severity of Traffic Accident in Montreal using Machine Learning. Preprints2024, 2024050816. https://doi.org/10.20944/preprints202405.0816.v1
Muktar, B.; Fono, V. Towards Safer Roads: Predicting the Severity of Traffic Accident in Montreal using Machine Learning. Preprints 2024, 2024050816. https://doi.org/10.20944/preprints202405.0816.v1
Muktar, B.; Fono, V. Towards Safer Roads: Predicting the Severity of Traffic Accident in Montreal using Machine Learning. Preprints2024, 2024050816. https://doi.org/10.20944/preprints202405.0816.v1
APA Style
Muktar, B., & Fono, V. (2024). Towards Safer Roads: Predicting the Severity of Traffic Accident in Montreal using Machine Learning. Preprints. https://doi.org/10.20944/preprints202405.0816.v1
Chicago/Turabian Style
Muktar, B. and Vincent Fono. 2024 "Towards Safer Roads: Predicting the Severity of Traffic Accident in Montreal using Machine Learning" Preprints. https://doi.org/10.20944/preprints202405.0816.v1
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) shows 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 to 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 the city of Montreal. This solution uses dataset of traffic accidents that occurred in Montreal between 2012 and 2021. 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. When evaluating the prediction model, performance metrics such as precision, recall, F1 score, and accuracy are taken into account. 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 and the Flask Python framework.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
