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在基于知识增强的交通预测方面，现有知识增强方法难以表征交通知识时间动态性。因此，本文提出关系-属性时序知识表示(RAT-KR),据此构建时序交通知识图谱(RAT-KG)。在此基础上，设计时间感知图注意力知识嵌入模块(TGA-KE),在注意力计算中显式引入时间信息以学习关系权重的动态变化，并将该模块以特征融合方式接入GWNet与STAEFormer,形成TGA-GWNet与TGA-STAEFormer两种知识增强模型。实验结果显示，所提方法在提升预测精度的同时，加快了模型收敛，并为预测结果提供了更直观的解释。
Abstract：In the field of knowledge-enhanced traffic prediction, existing knowledge enhancement methods struggle to represent the temporal dynamics of traffic knowledge. Therefore, this paper proposes a Relation-Attribute Temporal Knowledge Representation(RAT-KR), based on which a temporal traffic knowledge graph(RAT-KG) is constructed. Building upon this, a Time-aware Graph Attention Knowledge Embedding module(TGA-KE) is designed, explicitly incorporating temporal information into attention calculations to learn the dynamic changes of relationship weights. This module is then integrated into GWNet and STAEFormer using feature fusion, resulting in two knowledge-enhanced models: TGA-GWNet and TGA-STAEFormer. Experimental results show that the proposed method improves prediction accuracy while accelerating model convergence and providing a more intuitive explanation for the prediction results.
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基本信息:
DOI：10.16002/j.cnki.10090312.2026.01.017
中图分类号:U491.17
引用信息:
[1]任斌,王佳伟,吴亮弘,等.基于关系-属性时序知识增强的交通预测模型[J].东莞理工学院学报,2026,33(01):73-80.DOI:10.16002/j.cnki.10090312.2026.01.017.
基金信息:
国家自然科学基金面上项目(62273096); 广东省普通高校重点领域项目(2025ZDZX3039、2025ZDZX3061); 广东省普通高校创新团队项目(2024KCXTD046)
2025-04-29
2025
2025-08-31
2025
1
2026-02-25
2026-02-25