Real-Time Risk Identification of Rear-End Conflicts at Unsignalized Intersections

Rear-end collisions at unsignalized intersections remain a persistent issue in urban traffic environments, particularly at stop-controlled junctions. This study develops a real-time predictive model aimed at identifying potential rear-end conflicts, employing Deep & Cross Network Version 2 (DCNV2) to improve prediction accuracy. The methodology comprises three main components: data acquisition, model development, and interpretability analysis. Real-time vehicle trajectory data such as speed, inter-vehicle distance, and interaction behavior are collected and preprocessed before being analyzed using the DCNV2 model to uncover patterns associated with conflict risk. The model integrates cross-feature interactions to enhance predictive performance. Evaluation metrics, including accuracy, recall, and area under the curve (AUC), demonstrate that DCNV2 outperforms conventional classifiers such as logistic regression and support vector machines. To further evaluate model interpretability, SHapley Additive exPlanations (SHAP) are applied, revealing that short following distances, large speed differentials, and high traffic volumes on major roads are primary contributors to rear-end conflict risk. The findings provide actionable insights to inform proactive traffic safety strategies, particularly in urban areas where limited signalization or manual control exposes drivers to increased uncertainty. This predictive framework supports the development of real-time safety interventions and contributes to more effective risk mitigation at critical locations within the traffic network.