Road Accident Severity Prediction Model Using Machine Learning on Traffic and Environmental Factors

Abstract: Despite advancements in vehicle safety design, road accidents remain unavoidable and road accidents are still happening in both rural and urban areas because the increasing number of vehicles and multiple contributing factors. While rash driving may cause life and death situations, factors such as weather conditions, road type, traffic density, and seasonal changes also significantly influence accident severity. This project mainly applies machine learning techniques to predict the extent of danger resulting from road accidents by analyzing these influencing parameters. Random Forest and XGBoost models are used due to their effectiveness in handling complex and large- scale accident data and it also gives exact accuracy results of the road accidents. The proposed system helps us to identify high-risk conditions and supports traffic authorities and gives emergency response units in taking proactive safety measures. Overall, this work presents the practical approach to enhancing the road accidents through machine learning-based accident severity prediction.

Keywords: Machine Learning, Road Accident Severity Prediction, Random Forest, XGBoost, Predictive Risk Analysis.