Building the Framework for Road Accident Prediction for India using Enhanced SVM Techniques

Road accidents in India present a significant challenge, causing substantial loss of life and economic damage. Despite various safety measures, predicting road accidents remains difficult due to the complex interplay of factors such as traffic congestion, road conditions, and driver behavior. This paper proposes a framework for road accident prediction in India using Enhanced Support Vector Machine (SVM) techniques. By addressing challenges such as class imbalance, high-dimensional data, and non-linear relationships, this research improves upon traditional SVM models. The proposed model utilizes feature engineering, kernel tuning, and hybrid approaches to provide accurate accident predictions. Using real-world accident data from Indian roads, the enhanced SVM framework demonstrates superior performance compared to traditional models, offering a robust tool for policymakers and traffic management authorities. The enhanced SVM model achieved the Accuracy: 90.3%, Precision: 87.5% Recall: 79.8%, F1-Score: 83.4%, AUC-ROC: 92.1%. Future research in this area should prioritize the integration of real-time data from Internet of Things (IoT) sensors and advanced traffic management systems to significantly enhance the model's predictive capabilities.