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Traffic incident duration prediction based on deep learning methods
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DOI：10.1080/17517575.2024.2448828.png)
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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.
Keyword:
Traffic congestion
incident management
incident duration time prediction
intelligent transportation system
machine learning
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3.9
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2.8K
被引数:
1.8K
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