This study proposes a multi‐perspective fusion model for operating speed prediction based on knowledge‐enhanced graph neural networks, named RoadGNN‐S. By utilizing message passing and multi‐head self‐attention mechanisms, RoadGNN‐S can effectively capture the coupling impacts of multi‐perspective alignment elements (i.e., two‐dimensional design, 2.5‐dimensional driving, and three‐dimensional spatial perspectives). The results of driving simulation data show that root mean squared error, mean absolute error, mean absolute percentage error, and R‐squared values of RoadGNN‐S are superior to those of other classic deep learning algorithms. Then, prior knowledge (i.e., highway geometry supply, driver expectations, and vehicle dynamics) is introduced into RoadGNN‐S, and the models’ prediction accuracy and transferability are verified by field observation experiments. Compared to the above data‐driven models, knowledge‐enhanced RoadGNN‐S effectively avoids the fundamental errors, improving the R‐squared value in predicting passenger cars’ and trucks’ operating speed by 7.9% and 10.7%, respectively. The findings of this study facilitate the intelligent highway geometric design with multi‐perspective fusion and knowledge enhancement techniques.

中文翻译：

---

一种使用知识增强图神经网络的高速公路运行速度预测多视角融合模型


本研究提出了一种基于知识增强图神经网络的多视角融合速度预测模型，名为 RoadGNN-S。通过利用消息传递和多头自我注意机制，RoadGNN-S 可以有效地捕捉多视角对齐元素（即二维设计、2.5 维驾驶和三维空间视角）的耦合影响。驾驶仿真数据结果表明，RoadGNN-S 的均方根误差、平均绝对误差、平均绝对百分比误差和 R 平方值优于其他经典深度学习算法。然后，将先验知识（即高速公路几何供应、驾驶员期望和车辆动力学）引入 RoadGNN-S，并通过现场观察实验验证模型的预测准确性和可传递性。与上述数据驱动模型相比，知识增强的 RoadGNN-S 有效避免了基本误差，在预测乘用车和卡车运行速度时，R 平方值分别提高了 7.9% 和 10.7%。本研究结果通过多视角融合和知识增强技术促进了智能公路几何设计。