Accurate real-time traffic forecasting is a core technological problem against the implementation of the intelligent transportation system. However, it remains challenging considering the complex spatial and temporal dependencies among traffic flows. In the spatial dimension, due to the connectivity of the road network, the traffic flows between linked roads are closely related. In the temporal dimension, although there exists a tendency among adjacent time points, the importance of distant time points is not necessarily less than that of recent ones, since traffic flows are also affected by external factors. In this study, an attention temporal graph convolutional network (A3T-GCN) was proposed to simultaneously capture global temporal dynamics and spatial correlations in traffic flows. The A3T-GCN model learns the short-term trend by using the gated recurrent units and learns the spatial dependence based on the topology of the road network through the graph convolutional network. Moreover, the attention mechanism was introduced to adjust the importance of different time points and assemble global temporal information to improve prediction accuracy. Experimental results in real-world datasets demonstrate the effectiveness and robustness of the proposed A3T-GCN. We observe the improvements in RMSE of 2.51–46.15% and 2.45–49.32% over baselines for the SZ-taxi and Los-loop, respectively. Meanwhile, the Accuracies are 0.95–89.91% and 0.26–10.37% higher than the baselines for the SZ-taxi and Los-loop, respectively.

中文翻译：

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A3T-GCN：用于交通预测的注意力时间图卷积网络

准确的实时交通预测是阻碍智能交通系统实施的核心技术问题。然而，考虑到交通流之间复杂的空间和时间依赖性，它仍然具有挑战性。在空间维度上，由于路网的连通性，连接道路之间的交通流量密切相关。在时间维度上，虽然相邻时间点之间存在趋势，但远时间点的重要性并不一定低于最近时间点，因为交通流量也受到外部因素的影响。在这项研究中，提出了一种注意力时间图卷积网络（A3T-GCN）来同时捕获交通流中的全局时间动态和空间相关性。A3T-GCN 模型通过使用门控循环单元学习短期趋势，并通过图卷积网络学习基于道路网络拓扑结构的空间依赖性。此外，还引入了注意力机制来调整不同时间点的重要性并组装全局时间信息以提高预测精度。真实世界数据集中的实验结果证明了所提出的 A3T-GCN 的有效性和鲁棒性。我们观察到，对于 SZ-taxi 和 Los-loop，RMSE 分别比基线提高了 2.51-46.15% 和 2.45-49.32%。同时，准确率分别比 SZ-taxi 和 Los-loop 的基线高 0.95-89.91% 和 0.26-10.37%。