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What is Application of Explainable Machine Learning For Real-Time Safety Analysis about?

This study explores the application of explainable machine learning for real-time safety analysis in connected vehicle environments, focusing on conflict-based safety evaluation. Utilizing machine learning models, particularly Random Forest, the research reveals significant impacts of traffic flow features on real-time safety, with SHAP being employed to enhance model interpretability. The findings indicate that as the market penetration rate of connected vehicles increases, the performance of safety evalua

Author

qudrat1995r

Language

EN