In recent decades, the rise in road traffic accidents poses a significant challenge to public health and infrastructure, especially in swiftly urbanizing regions such as India. Traditional reactive strategies for accident prevention have proven insufficient in proactively mitigating such occurrences. This research emphasizes the crucial need for intelligent predictive systems by introducing a data-driven framework for predicting road accidents using ML and data mining techniques. The approach involves preprocessing historical accident data to eliminate noise and missing values, followed by the training of ranking models like Decision Trees, Random Forest, and Support Vector Machines. Evaluation of model performance includes standards such as correctness, precision, recollect, and F1-score. Through integration with a Streamlit-based application, the model allows for real-time forecasting and visualization of high-risk accident areas. Findings reveal that Random Forest achieved the highest accuracy at 88%, underscoring the potential of the framework to support urban planners, traffic authorities, and emergency responders in implementing preventive measures. This investigation lays the groundwork for scalable, intelligent solutions to enhance transportation safety.
Girish Kumar D (Tue,) studied this question.