用于时空交通预测的通用语义知识注入
General Semantic Knowledge Infusion for Spatio-Temporal Traffic Forecasting
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中文总结 AI 辅助
本研究提出一种时空预测框架，通过融合通用知识图谱与传感器网络数据，提升GNN的交通预测精度，且验证了外部知识对预测效果的普遍增益作用。
中文摘要 AI 辅助
尽管图神经网络（GNN）在时空交通预测领域已取得显著进展，但其性能受限于仅依赖传感器邻近性或道路网络拓扑结构。本文提出一种时空预测框架，旨在融入各类形式的知识，以提升传感器层面的环境上下文理解能力。该框架利用通用知识图谱（如Wikidata）构建交通传感器周边的语义子图，并生成知识图谱嵌入，以捕捉有意义的关系，例如附近的兴趣点、行政层级及地点的功能角色。随后，将这些嵌入与传统交通传感器图融合，生成由语义信息驱动的额外邻接矩阵，使GNN能够学习物理连接之外的语义上下文。本研究与过往工作存在两处关键差异：其一，未提出新型GNN架构，而是验证了外部知识对预测精度的普遍影响；其二，对成熟的交通预测方法开展的实验表明，外部知识提供了仅街道网络数据无法传递的额外信息。实验结果显示，通过数据融合整合通用知识图谱与传感器网络的数据，可提升交通预测模型的精度，并为增强模型可解释性提供了潜在途径。
英文摘要
Although Graph Neural Networks (GNNs) have made significant advances in spatio-temporal traffic forecasting, their performance is limited when they rely solely on sensor proximity or road-network topology. This paper presents a spatio-temporal prediction framework, developed to incorporate knowledge in various forms. This framework aims to improve sensor-level, contextual understanding of the environment. A general-purpose knowledge graph (e.g., Wikidata) is used to create semantic subgraphs around traffic sensors and generate knowledge graph embeddings that capture meaningful relationships, such as nearby points of interest, administrative hierarchies, and the functional roles of locations. These embeddings are then fused with conventional traffic sensor graphs to provide additional adjacency matrices informed by semantics. This allows GNNs to learn the semantic context beyond physical connectivity. This study differs from previous research in two key ways. Firstly, rather than proposing a novel GNN architecture, it demonstrates the general impact of external knowledge on prediction accuracy. Secondly, experiments with well-established traffic forecasting approaches show that external knowledge provides additional information that street network data alone cannot convey. The results show that integrating data from general-purpose knowledge graphs and sensor networks through data fusion can enhance the prediction accuracy of traffic forecasting models, and offers a potential pathway toward improved interpretability.
发表机构
- Kiel University（基尔大学）
- GEOMAR Helmholtz Centre for Ocean Research Kiel（基尔亥姆霍兹海洋研究中心）
- ZBW – Leibniz Information Centre for Economics（莱布尼茨经济信息中心（ZBW））
机构由 AI 辅助整理，请以论文原文为准。
