| 摘 要: 针对现有交通流预测模型在预测精度上的不足,提出一种基于注意力机制的图模型。首先,利用多头注意力机制在交通图中编码高阶邻域结构,提取交通网络中的高阶空间特征。然后,嵌入长距离时间结构注意力机制提取长期性的历史周期信息。模型采用注意力机制替代传统的局部卷积核结构,可以有效提取长距离时空依赖关系。在METR-LA(洛杉矶路网)、PeMS-BAY(加州湾区路网)、PeMS-S(加州小型路网)三个真实的交通数据集上进行实验证明,模型在预测未来60 min的交通流精度上较传统深度学习方法,RMSE (均方根误差)平均降低3.1%、3.9%和1.8%,表明所提模型的长时间预测能力优势明显。 |
| 关键词: 注意力机制;图模型;时空依赖;交通流预测 |
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中图分类号: TP391
文献标识码: A
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| 基金项目: 湖南工业职业技术学院应用技术专项课题(GYKYYJ202008) |
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| A Traffic Flow Prediction Model Based on Graph Attention Mechanism |
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ZHOU Anzhong, XIE Dingfeng
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(Department of Information Engineering, Hunan Industry Polytechnic, Changsha 410208, China)
sprite4@163.com; coolboyxie@163.com
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| Abstract: This paper proposes a graph model based on attention mechanism to address the accuracy issues of existing traffic flow prediction models. Firstly, the multi-head attention mechanism is used to encode high-order neighborhood structures in the traffic map and extract high-order spatial features in the traffic network. Then, a long-distance time structure attention mechanism is embedded to extract long-term historical cycle information. The model uses attention mechanism to replace traditional local convolutional kernel structure, which can effectively extract long-distance spatio-temporal dependencies. Experiments are carried out on three real traffic data sets, METR-LA (Road Network of Los Angeles), PeMSBAY (Bay Area Road Network of California), and PeMS-S (Small Road Network of California). Experimental results show that the RMSE (Root Mean Square Error) of the traffic flow prediction accuracy of the proposed model in the next 60 minutes is 3.1% , 3.9% , and 1.8% lower on average than that of traditional deep learning method, which indicates that the proposed model has obvious advantages in long-term prediction ability. |
| Keywords: attention mechanism; graph model; spatio-temporal dependencies; traffic flow prediction |
