Short-term traffic speed forecasting based on graph attention temporal convolutional networks
Short-term traffic speed forecasting based on graph attention temporal convolutional networks
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DOI：
10.1016/j.neucom.2020.06.001
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发表时间：
2020-10-14
期刊：
影响因子：
6
通讯作者：
Yuan, Wei
中科院分区：
文献类型：
--
作者：
Guo, Ge;Yuan, Wei
准确、及时的交通预测对智能交通管理具有重要意义。然而，现有的方法模型的时间和空间特征的交通流不足。为了解决这些局限性，本文提出了一种基于图注意力网络（GAT）和时间卷积网络（TCN）的深度学习流量预测框架，称为图注意力时间卷积网络（GATCN）。更具体地说，GATCN处理的空间特征的GAT，和时间特征的TCN。该层融合了GAT和TCN，使该模型能够学习交通流的时空特征，同时考虑外源因素。此外，图中的节点可以通过堆叠多个层来捕获其邻域的信息。通过对真实数据集的测试，对所提出的方法的精度和鲁棒性进行了评估。结果表明，该模型优于其他基线。(C)2020爱思唯尔B.V.保留所有权利。
Accurate and timely traffic forecasting is significant for intelligent transportation management. However, existing approaches model the temporal and spatial features of traffic flow inadequately. To address these limitations, a novel deep learning traffic forecasting framework based on graph attention network (GAT) and temporal convolutional network (TCN) is presented in this paper, termed as graph attention temporal convolutional networks (GATCN). More specifically, GATCN deal with the spatial features by GAT, and the temporal features by TCN. The layer fused by GAT and TCN enables the proposed model to learn the spatio-temporal characteristics that lie in traffic flow, while considering exogenous factors. In addition, nodes in the graph can capture the information of their neighborhoods by stacking multiple layers. Precision and robustness of the proposed method have been evaluated through testing on the real-world dataset. Results show that the proposed model outperforms other baselines. (C) 2020 Elsevier B.V. All rights reserved.