Traffic incident duration prediction based on deep learning methods

Traffic congestion increases travel time and pollution, necessitating precise incident handling time predictions. This study addresses data imbalance, feature granularity, and label inconsistencies using K-means clustering, re-labelling, and concept hierarchy refinements. A predictive framework integrates classification and regression models to estimate incident duration for various accident types. Results show Random Forest achieves 94.32% accuracy for clearance time, XGBoost a mean absolute error (MAE) of 6.44 minutes for response time, and Support Vector Machine with Gradient Boosting Regression Trees a MAE of 13.3 minutes for total incident duration. These results support quick countermeasures, reduce congestion, and inform drivers via highway signs.