HERMES：用于信号交叉口交通冲突预测的异构边缘关系多头嵌入式SSM注意力模型
HERMES: Heterogeneous Edge-Relational Multi-Head Embedded SSM Attention for Traffic Conflict Prediction at Signalized Intersections
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中文总结 AI 辅助
研究针对交通冲突预测，提出HERMES异构边缘关系图神经网络，利用特定关系注意力等方法，在多指标上表现优异，优于基线模型，联合训练提升目标站点性能，为信号交叉口路边安全监测提供支持。
中文摘要 AI 辅助
替代安全措施（SSMs）可实现主动交通安全评估，但现有方法存在局限性。本研究将交通冲突评估表述为时间异构场景图分类，并提出HERMES，一种具有SSM信息多头注意力的异构边缘关系图神经网络。车辆和行人表示为异构节点，不同交互编码为特定关系边。通过特定关系注意力、动态节点-边更新等联合估计场景级冲突概率。实验表明，增强版HERMES在多个指标上表现出色，优于基线模型，还通过联合源-目标训练提升了目标站点性能，证明了相关改进对场景级冲突分类及可转移路边安全监测的支持。
英文摘要
Surrogate safety measures (SSMs) enable proactive traffic safety assessment, but many existing methods evaluate pairwise interactions independently or flatten multi-agent scenes into fixed feature vectors, limiting their ability to represent heterogeneous interaction structure and evolving scene-level risk. This study formulates traffic conflict assessment as temporal heterogeneous scene-graph classification and proposes HERMES, a heterogeneous edge-relational graph neural network with SSM-informed multi-head attention. Vehicles and pedestrians are represented as heterogeneous nodes, while vehicle-vehicle, vehicle-pedestrian, and pedestrian-pedestrian interactions are encoded as relation-specific edges with continuous kinematic and surrogate-safety descriptors. Relation-specific attention, dynamic node-edge updates, safety-aware graph pooling, and temporal sequence learning are jointly used to estimate scene-level conflict probability. HERMES was evaluated using 109,028 trajectory-derived sequences from a signalized urban intersection and tested on an independently collected comparable intersection dataset. Enhanced HERMES achieved an AUC-ROC of 0.9898 +/- 0.0013, an AUC-PR of 0.9412 +/- 0.0067, and an F1 score of 0.8449 +/- 0.0103. At a 5% false-alarm rate, it detected 95.7% of conflict sequences, outperforming the strongest Transformer baseline and XGBoost. In zero-shot external evaluation, HERMES achieved an AUC-ROC of 0.9752 and an AUC-PR of 0.7829. Joint source-target training further improved target-site performance with limited target-site data. These findings show that preserving heterogeneous interaction topology, safety-informed edge semantics, and short-term temporal evolution improves scene-level conflict classification and supports transferable roadside safety monitoring at signalized intersections.
