Structure–behavior integrated risk prediction of vehicle groups using Graph Neural Networks

Accurate identification of vehicle-group-level traffic risk is important for intelligent transportation safety management. Existing risk-prediction studies have mainly focused on individual vehicles, pairwise interactions, or aggregated surrogate safety indicators, while the role of group-level structure–behavior coupling remains insufficiently examined. To address this issue, this paper proposes a leakage-aware structure–behavior graph learning framework for Vehicle Group (VG) risk modeling. First, time-resolved VG graphs are constructed from high-frequency MAGIC trajectory data using an impact-induced grouping strategy with controlled supplementary spatial adjacency. Second, node-level surrogate risk states are defined using inverse Time-to-Collision (iTTC) and activated Post-Encroachment Time (PET), and then aggregated into VG-level surrogate risk labels through a group-risk ratio. Third, structural descriptors and behavioral features are integrated within a Graph Attention Network (GAT) for VG-level risk classification. To improve methodological transparency, the revised framework explicitly justifies the surrogate-labeling thresholds using empirical distributions, threshold tradeoff curves, and sensitivity analyses. It also includes leakage-audit experiments to examine whether model performance is driven by label-proximal surrogate quantities. Experiments on the MAGIC dataset show that the proposed model achieves strong and stable performance under repeated random seeds, with F1-score = 0.960 ± 0.002, ROC-AUC = 0.949 ± 0.008, PR-AUC = 0.991 ± 0.001, and Brier score = 0.058 ± 0.002. Compared with linear, neural, tree-based, and graph-based baselines, the proposed model provides competitive risk-identification performance, particularly in recall, false-negative control, and positive-class ranking. Additional robustness, prospective-prediction, and interpretability analyses indicate that the learned structure–behavior representation remains informative under temporal and sampling-frequency shifts and can highlight risk-relevant vehicles within VGs. The findings should be interpreted as evidence of structure-aware predictive association under a surrogate-labeling framework, rather than as proof that VG risk is inherently structural or that the model is deployment-ready. Overall, this study provides a more transparent and auditable graph-learning approach for VG-level surrogate risk modeling, offering decision-support insights for future CAV-oriented traffic safety management subject to external validation and deployment-oriented computational testing.