Calibrated Confidence Learning For Large-scale Real-time Crash And Severity Prediction

This article discusses the challenges of real-time crash and severity prediction and introduces a novel modeling technique to address these challenges. The technique utilizes real-time traffic and weather data and incorporates local model regularization and post-calibration to improve accuracy. Results demonstrate excellent performance in predicting different severity levels for various crash types.