A. Dhamaniya

Predicting High-Risk Conflicts on Highways Using Hybrid Deep Learning Resampling and Explainable Artificial Intelligence

Three machine-learning algorithms with six resampling techniques are developed, including a novel deep learning framework that integrates conditional tabular generative adversarial network (CTGAN)–based synthetic data generation with strategic undersampling, significantly enhances high-risk conflict prediction and supports more effective driver alerts in heterogeneous traffic conditions.