Network-wide traffic forecasting is a critical component of modern intelligent transportation systems for urban traffic management and control. With the rise of artificial intelligence, many recent studies attempted to use deep neural networks to extract comprehensive features from traffic networks to enhance prediction performance, given the volume and variety of traffic data has been greatly increased. Considering that traffic status on a road segment is highly influenced by the upstream/downstream segments and nearby bottlenecks in the traffic network, extracting well-localized features from these neighboring segments is essential for a traffic prediction model. Although the convolution neural network or graph convolution neural network has been adopted to learn localized features from the complex geometric or topological structure of traffic networks, the lack of flexibility in the local-feature extraction process is still a big issue. Classical wavelet transform can detect sudden changes and peaks in temporal signals. Analogously, when extending to the graph/spectral domain, graph wavelet can concentrate more on key vertices in the graph and discriminatively extract localized features. In this study, to capture the complex spatial-temporal dependencies in network-wide traffic data, we learn the traffic network as a graph and propose a graph wavelet gated recurrent (GWGR) neural network. The graph wavelet is incorporated as a key component for extracting spatial features in the proposed model. A gated recurrent structure is employed to learn temporal dependencies in the sequence data. Comparing to baseline models, the proposed model can achieve state-of-the-art prediction performance and training efficiency on two real-world datasets. In addition, experiments show that the sparsity of graph wavelet weight matrices greatly increases the interpretability of GWGR.

全网交通预测是用于城市交通管理和控制的现代智能交通系统的重要组成部分。随着人工智能的兴起，鉴于交通数据的数量和种类已经大大增加，许多最近的研究尝试使用深度神经网络从交通网络中提取综合特征以增强预测性能。考虑到路段的交通状况受交通网络中上游/下游路段和附近瓶颈的严重影响，因此从这些相邻路段中提取定位良好的特征对于交通预测模型至关重要。尽管已经采用卷积神经网络或图卷积神经网络从交通网络的复杂几何或拓扑结构中学习局部特征，但是局部特征提取过程中缺乏灵活性仍然是一个大问题。经典的小波变换可以检测时间信号中的突然变化和峰值。类似地，当扩展到图/谱域时，图小波可以更多地集中在图中的关键顶点上，并有区别地提取局部特征。在这项研究中，为了捕获网络范围内交通数据中复杂的时空依赖性，我们将交通网络作为图来学习，并提出了图小波门控递归（GWGR）神经网络。图小波被并入为所提出模型中提取空间特征的关键组件。门控循环结构用于学习序列数据中的时间依赖性。与基线模型相比，所提出的模型可以在两个真实的数据集上实现最新的预测性能和训练效率。此外，实验表明，图小波权重矩阵的稀疏性大大提高了GWGR的可解释性。