Conference
Open access
2026
Using Bayesian Optimized XGBoost for Predicting Traffic Accident Duration
The proposed model incorporates temporal, weather, environmental, and infrastructure features from the traffic accident dataset, and XGBoost hyperparameters are tuned using Bayesian optimization, and outperforms conventional machine learning algorithms in both recall and F1 score.
Shi-Kang Zhong
· ITM Web of Conferences · 0 citations