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A Machine Learning Framework for Vehicle Road Safety Risk Prediction and Identification

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The Random Forest model achieved 96% accuracy in predicting vehicle accident risks, enhancing road safety.
Utilizing machine learning, the framework integrates traffic datasets and preprocessing techniques for reliable modeling.
Advanced features like lighting conditions and vehicle types help identify risk-prone scenarios in real time.
The model's interpretable design may support traffic planners in implementing automated safety strategies.

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Data-driven model predicts accident risk in vehicles, indicating potential for proactive safety measures.

Puneet Garg (2025) studied this question.

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