The traffic state in an urban transportation network is determined via spatio-temporal traffic propagation. In early traffic forecasting studies, time-series models were adopted to accommodate autocorrelations between traffic states. The incorporation of spatial correlations into the forecasting of traffic states, however, involved a computational burden. Deep learning technologies were recently introduced to traffic forecasting in order to accommodate the spatio-temporal dependencies among traffic states. In the present study, we devised a novel graph-based neural network that expanded the existing graph convolutional neural network (GCN). The proposed model allowed us to differentiate the intensity of connecting to neighbor roads, unlike existing GCNs that give equal weight to each neighbor road. A plausible model architecture that mimicked real traffic propagation was established based on the graph convolution. The domain knowledge was efficiently incorporated into a neural network architecture. The present study also employed a generative adversarial framework to ensure that a forecasted traffic state could be as realistic as possible considering the joint probabilistic density of real traffic states. The forecasting performance of the proposed model surpassed that of the original GCN model, and the estimated adjacency matrices revealed the hidden nature of real traffic propagation.

城市交通网络中的交通状态是通过时空交通传播来确定的。在早期的交通预测研究中，采用时间序列模型来适应交通状态之间的自相关。然而，将空间相关性并入交通状态的预测涉及计算负担。深度学习技术最近被引入交通预测中，以适应交通状态之间的时空依赖性。在本研究中，我们设计了一种新颖的基于图的神经网络，扩展了现有的图卷积神经网络（GCN）。所提出的模型使我们能够区分连接到相邻道路的强度，这与现有的GCN赋予每个相邻道路相同的权重不同。基于图卷积，建立了模仿实际流量传播的合理模型架构。领域知识被有效地整合到了神经网络架构中。考虑到实际交通状态的联合概率密度，本研究还采用了生成对抗性框架，以确保预测的交通状态尽可能真实。所提出模型的预测性能超过了原始GCN模型的预测性能，并且估计的邻接矩阵揭示了实际流量传播的隐藏性质。考虑到实际交通状态的联合概率密度，本研究还采用了生成对抗性框架，以确保预测的交通状态尽可能真实。所提出模型的预测性能超过了原始GCN模型的预测性能，并且估计的邻接矩阵揭示了实际流量传播的隐藏性质。考虑到实际交通状态的联合概率密度，本研究还采用了生成对抗性框架，以确保预测的交通状态尽可能真实。所提出模型的预测性能超过了原始GCN模型的预测性能，并且估计的邻接矩阵揭示了实际流量传播的隐藏性质。