A Vehicle Trajectory-Based Sequential Learning Framework for Rear-End Conflict Detection on Expressways

Real-time traffic safety analysis is necessary to determine proactive risks associated with traffic conflicts. This study presents a driving-risk detection framework based on high-precision naturalistic vehicle trajectory data to support host vehicle driver-warning systems. A rear-end conflict identification approach based on time-to-collision (TTC) is employed to detect vehicle interactions as safe or unsafe. To capture temporal driving patterns, frame-level observations are transformed into sequential samples using a sliding-window strategy while preserving the natural class imbalance of real-world traffic data. Several conventional machine learning models, including CatBoost, LightGBM, XGBoost, Random Forest, Extra Trees, Decision Tree, and SVM, as well as recurrent deep learning models such as Simple RNN, LSTM, and GRU, are evaluated using leave-one-subject-out cross-validation across seven expressways. Among the evaluated models, the Simple RNN achieves a recall of 99.12% and an F1-score of 98.48%, outperforming the conventional machine learning models. Its predictive performance is comparable to that of LSTM and GRU while offering lower inference latency, making it suitable for real-time deployment. A SHAP-based Explainable AI analysis is conducted to identify the most influential factors in conflict detection and to provide insight into model predictions. The analysis supports that host vehicle speed, preceding vehicle speed, and inter-vehicle gap are the primary determinants of rear-end conflict risk. This proactive and interpretable framework, evaluated offline on naturalistic trajectory data, demonstrates strong potential for integration into real-time driver-warning systems.