Yu Xian, Bao Yin-Xin, Shi Quan
School of Information Science and Technology, Nantong University, Nantong 226019, China.
School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China.
Heliyon. 2023 Sep 11;9(9):e19927. doi: 10.1016/j.heliyon.2023.e19927. eCollection 2023 Sep.
Nowadays, as a crucial component of intelligent transportation systems, traffic flow prediction has received extensive concern. However, most of the existing studies extracted spatial-temporal features with modules that do not differentiate with time and space, and failed to consider spatial-temporal heterogeneities. Furthermore, although previous works have achieved synchronous modeling of spatial-temporal dependencies, the consideration of temporal causality is still lacking in their graph structures. To address these shortcomings, a spatial-temporal heterogeneous and synchronous graph convolution network (STHSGCN) is proposed for traffic flow prediction. To be specific, separate dilated causal spatial-temporal synchronous graph convolutional networks (DCSTSGCNs) for various node clusters are designed to reflect spatial heterogeneity, different dilated causal spatial-temporal synchronous graph convolutional modules (DCSTSGCMs) for diverse time steps are deployed to take account of temporal heterogeneity. In addition, causal spatial-temporal synchronous graph (CSTSG) is proposed to capture temporal causality in spatial-temporal synchronous learning. We further conducted extensive experiments on four real-world datasets, and the results verified the consistent superiority of our proposed approach compared with various existing baselines.
如今，交通流预测作为智能交通系统的关键组成部分，受到了广泛关注。然而，现有的大多数研究使用的模块在提取时空特征时未对时间和空间进行区分，并且没有考虑到时空异质性。此外，尽管先前的工作已经实现了时空依赖性的同步建模，但在其图结构中仍然缺乏对时间因果关系的考虑。为了解决这些缺点，本文提出了一种用于交通流预测的时空异构同步图卷积网络（STHSGCN）。具体而言，针对不同的节点簇设计了单独的扩张因果时空同步图卷积网络（DCSTSGCN）以反映空间异质性，针对不同的时间步长部署了不同的扩张因果时空同步图卷积模块（DCSTSGCM）以考虑时间异质性。此外，还提出了因果时空同步图（CSTSG）以在时空同步学习中捕捉时间因果关系。我们进一步在四个真实世界数据集上进行了广泛的实验，结果验证了我们提出的方法与各种现有基线相比具有持续的优越性。
