Year: 2025
Type: article
Abstract: Road traffic accidents continue to be a critical global issue, causing substantial human suffering and economic costs. In recent years, predictive modeling has emerged as a promising direction for minimizing these risks by anticipating accidents and identifying key contributing factors. This review summarizes four notable studies in this area: an interpretable spatio–temporal multi-graph learning model (ASTMGCN), the use of CatBoost and BERT for accident type classification, a Random Forest–CNN ensemble (RFCNN) for severity prediction, and a CNN–BiLSTM–Attention model enhanced with DeepSHAP for risk assessment. These approaches are compared in terms of methodology, datasets, feature design, and interpretability. The findings indicate that hybrid and attention-based models generally outperform traditional methods, while explainability tools such as attention visualizations and SHAP values significantly improve trust and applicability in Intelligent Transportation Systems (ITS). Keywords :- Advances in Intelligent Transportation Systems (ITS), Machine learning (ML) and Deep learning (DL).
DOI: 10.55041/ijsrem52119
Authors: Durgeshwari Sahu, Prof. Ravikant Soni, Prof. Neelam Sharma
Language: English
Topic: Traffic and Road Safety
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4413612545