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| Spatial-temporal multi-graph convolution for traffic flow prediction by integrating knowledge graphs |
Jinye LI( ),Yongqiang LI*() |
| College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China |
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Abstract A spatial-temporal multi-graph convolution traffic flow prediction model by integrating static and dynamic knowledge graphs was proposed, as current traffic flow prediction methods focus on the spatial-temporal correlation of traffic information and fail to fully take into account the influence of external factors on traffic. An urban traffic knowledge graph and four road network topological graphs with distinct semantics were systematically constructed, drawing upon the road traffic information and the external factors. The urban traffic knowledge graph was inputted into the relational evolution graph convolutional neural network to realize the knowledge embedding. The traffic flow matrix and the knowledge embedding were integrated using the knowledge fusion module. The four road network topology graphs and the traffic flow matrix with fused knowledge were fed into the spatial-temporal multi-graph convolution module to extract spatiotemporal features, and the traffic flow prediction value was outputted through the fully connected layer. The model performance was evaluated on a Hangzhou traffic data set. Compared with the advanced baseline, the performance of the proposed model improved by 5.76%-10.71%. Robustness experiment results show that the proposed model has a strong ability to resist interference.
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Received: 20 September 2023
Published: 01 July 2024
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| Fund: 国家自然科学基金资助项目（62073294）；浙江省自然科学基金资助项目（LZ21F030003）. |
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Corresponding Authors:
Yongqiang LI
E-mail: 2924258232@qq.com;yqli@zjut.edu.cn
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融合知识图谱的时空多图卷积交通流量预测
现有的交通流量预测方法关注交通信息的时空相关性，未充分考虑外部因素对交通的影响，为此提出融合静态和动态知识图谱的时空多图卷积交通流量预测模型. 基于道路交通信息和外部因素，构建城市交通知识图谱和4个不同语义的路网拓扑图，将城市交通知识图谱输入关系演化图卷积神经网络，实现知识嵌入；使用知识融合模块将车流量矩阵与知识嵌入融合；将4个路网拓扑图和融合知识的车流量矩阵输入时空多图卷积模块，提取时空特征，通过全连接层输出交通流量预测值. 在杭州交通数据集上评估模型性能，与先进的基线模型对比，所提模型的性能提高了5.76%~10.71%. 鲁棒性实验结果表明，所提模型具有较强的抗干扰能力.
关键词：
智能交通,
交通流量预测,
城市交通知识图谱,
多图卷积神经网络,
知识融合模块,
路网拓扑图
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