Accurate and explainable short-term traffic forecasting is pivotal for making trustworthy decisions in advanced traffic control and guidance systems. Recently, deep learning approach, as a data-driven alternative to traffic flow model-based data assimilation and prediction methods, has become popular in this domain. Many of these deep learning models show promising predictive performance, but inherently suffer from a lack of interpretability. This difficulty largely originates from the inconsistency between the static input–output mappings encoded in deep neural networks and the dynamic nature of traffic phenomena. Under different traffic conditions, such as freely-flowing versus heavily congested traffic, different mappings are needed to predict the propagation of congestion and the resulting speeds over the network more accurately. In this study, we design a novel variant of the graph attention mechanism. The major innovation of this so-called dynamic graph convolution (DGC) module is that local area-wide graph convolutional kernels are dynamically generated from evolving traffic states to capture real-time spatial dependencies. When traffic conditions change, the spatial correlation encoded by DGC module changes as well. Using the DGC, we propose a multistep traffic forecasting model, the Dynamic Graph Convolutional Network (DGCN). Experiments using real freeway data show that the DGCN has a competitive predictive performance compared to other state-of-the-art models. Equally importantly, the prediction process in the DGCN and the trained parameters are indeed explainable. It turns out that the DGCN learns to mimic the upstream–downstream asymmetric information flow of typical road traffic operations. Specifically, there exists a speed-dependent optimal receptive field – which governs what information the DGC kernels assimilate – that is consistent with the back-propagation speed of stop-and-go waves in traffic streams. This implies that the learnt parameters are consistent with traffic flow theory. We believe that this research paves a path to more transparent deep learning models applied for short-term traffic forecasting.

准确，可解释的短期流量预测对于在高级流量控制和引导系统中做出可信赖的决策至关重要。最近，深度学习方法作为基于数据流模型的数据同化和预测方法的数据驱动替代方法，已在此领域流行。这些深度学习模型中的许多模型都显示出有希望的预测性能，但固有地缺乏解释性。这种困难很大程度上源于深度神经网络中编码的静态输入-输出映射与交通现象的动态性质之间的不一致。在不同的流量条件下，例如自由流动与严重拥塞的流量，需要使用不同的映射来更准确地预测拥塞的传播以及由此产生的网络速度。在这项研究中，我们设计了一种新的图注意力机制变体。这种所谓的动态图卷积（DGC）模块的主要创新之处在于，局部区域图卷积内核是根据不断演变的流量状态动态生成的，以捕获实时空间相关性。当交通状况发生变化时，由DGC模块编码的空间相关性也会发生变化。使用DGC，我们提出了一个多步骤流量预测模型，即动态图卷积网络（DGCN）。使用实际高速公路数据进行的实验表明，与其他最新模型相比，DGCN具有竞争性的预测性能。同样重要的是，DGCN中的预测过程和训练后的参数确实是可以解释的。事实证明，DGCN学会了模仿典型道路交通运营的上下游不对称信息流。具体来说，存在一个依赖于速度的最佳接收场，该场控制DGC内核吸收的信息，这与交通流中走走停停的波的反向传播速度一致。这意味着所学习的参数与交通流理论相一致。我们相信，这项研究为通向短期流量预测的更透明的深度学习模型铺平了道路。