Machine Learning-Based Prediction and Risk Assessment of Road Accidents in Bangladesh

Accidents that happen on the road in Bangladesh are very challenging to the life and financial integrity of the people. The proposed study is a machine learning framework that can be used to predict the severity of accidents using past and recent accident data. We used the information from the Accident Research Institute (2005-2015) and the Bangladesh Road Transport Authority (2023-2024). The models trained were Decision Tree, Random Forest, Balanced Random Forest, XGBoost, and Naive Bayes, with SHAP values used to interpret them. To be robust, the cross-validation was used, and the assessment was guided by category-specific confusion matrices. The Decision Tree model scored higher in the mean of F1-score than others, with a score of 0.871. To be usefully accessible, a Streamlit web application that reproduces real-time predictions was created. This study will help a policymaker understand the level of accident risks during times of day, types of vehicles, and weather conditions, to enable actionable intelligence.