Dynamic spatio-temporal dependencies and temporal patterns in traffic series are critical factors affecting traffic forecasting accuracy. Due to the intrinsic challenges of incorporating explicit, logical knowledge into the implicit black-box learning process of neural networks, only a few methods effectively use prior knowledge to improve the learning of traffic forecasting. To tackle this problem, we introduce a new approach called Knowledge-augmented and Time-aware Graph Convolutional Network, namely KaTaGCN. At its core, we have created a knowledge-augmented module that boosts the diffusion weights between closely related adjacent nodes in graph learning. This is achieved by implementing a new loss function. Then, to learn the periodic implicit relationship between these timestamps and traffic signals, the weights and biases are chosen adaptively to be trained based on the timestamps of each sample. Finally, a gated spatio-temporal mapping module regresses high-dimensional embedded features from spatial and temporal dimensions. KaTaGCN is structured without any attention mechanisms or recurrent neural networks. Extensive experimental results on six real-world public traffic datasets demonstrate that KaTaGCN achieves an average improvement of 4.29% in forecasting performance compared with suboptimal results.

中文翻译：

---

KaTaGCN：用于高效流量预测的知识增强和时间感知图卷积网络


交通序列中的动态时空依赖性和时间模式是影响交通预测准确性的关键因素。由于将显性的逻辑知识融入神经网络的隐式黑盒学习过程中存在固有的挑战，因此只有少数方法有效地利用先验知识来改进交通预测的学习。为了解决这个问题，我们引入了一种称为知识增强和时间感知图卷积网络的新方法，即 KaTaGCN。其核心是，我们创建了一个知识增强模块，可以提高图学习中密切相关的相邻节点之间的扩散权重。这是通过实施新的损失函数来实现的。然后，为了学习这些时间戳和交通信号之间的周期性隐式关系，根据每个样本的时间戳自适应地选择权重和偏差进行训练。最后，门控时空映射模块从空间和时间维度回归高维嵌入特征。 KaTaGCN 的结构没有任何注意机制或循环神经网络。对六个真实公共交通数据集的大量实验结果表明，与次优结果相比，KaTaGCN 的预测性能平均提高了 4.29%。