T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction
T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction
复制标题
T-GCN：用于流量预测的时态图卷积网络
DOI：
10.1109/tits.2019.2935152
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发表时间：
2020-09-01
影响因子：
8.5
通讯作者：
Li, Haifeng
中科院分区：
文献类型：
--
作者：
Zhao, Ling;Song, Yujiao;Li, Haifeng
准确、实时的交通预测在智能交通系统中起着重要作用，对城市交通规划、交通管理和交通控制具有重要意义。但由于受城市道路网络拓扑结构和随时间动态变化规律的制约，交通量预测一直被认为是一个“开放”的科学问题。为了同时捕获空间和时间依赖性，我们提出了一种新的基于神经网络的流量预测方法，时间图卷积网络（T-GCN）模型，它结合了图卷积网络（GCN）和门控递归单元（GRU）。具体地说，GCN用于学习复杂的拓扑结构以捕获空间依赖性，而门控递归单元用于学习交通数据的动态变化以捕获时间依赖性。然后，将T-GCN模型应用于城市道路网络的交通预测。实验结果表明，T-GCN模型能够较好地反映交通流数据的时空相关性，预测结果优于现有的交通流预测方法。我们的T-GCN张量流实现可在https://www.github.com/lehaifeng/T-GCN上获得。
Accurate and real-time traffic forecasting plays an important role in the intelligent traffic system and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an "open" scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time. To capture the spatial and temporal dependences simultaneously, we propose a novel neural network-based traffic forecasting method, the temporal graph convolutional network (T-GCN) model, which is combined with the graph convolutional network (GCN) and the gated recurrent unit (GRU). Specifically, the GCN is used to learn complex topological structures for capturing spatial dependence and the gated recurrent unit is used to learn dynamic changes of traffic data for capturing temporal dependence. Then, the T-GCN model is employed to traffic forecasting based on the urban road network. Experiments demonstrate that our T-GCN model can obtain the spatio-temporal correlation from traffic data and the predictions outperform state-of-art baselines on real-world traffic datasets. Our tensorflow implementation of the T-GCN is available at https://www.github.com/lehaifeng/T-GCN.