Zhao Zhenzhen, Shen Guojiang, Zhou Junjie, Jin Junchen, Kong Xiangjie
College of Computer Science and Technology, Zhejiang University of Technology, HangZhou, China.
College of Control Science and Engineering, Zhejiang University, HangZhou, China.
PeerJ Comput Sci. 2023 Jul 4;9:e1450. doi: 10.7717/peerj-cs.1450. eCollection 2023.
Accurate traffic forecasting plays a critical role in the construction of intelligent transportation systems. However, due to the across road-network isomorphism in the spatial dimension and the periodic drift in the temporal dimension, existing traffic forecasting methods cannot satisfy the intricate spatial-temporal characteristics well. In this article, a spatial-temporal hypergraph convolutional network for traffic forecasting (ST-HCN) is proposed to tackle the problems mentioned above. Specifically, the proposed framework applies the K-means clustering algorithm and the connection characteristics of the physical road network itself to unify the local correlation and across road-network isomorphism. Then, a dual-channel hypergraph convolution to capture high-order spatial relationships in traffic data is established. Furthermore, the proposed framework utilizes a long short-term memory network with a convolution module (ConvLSTM) to deal with the periodic drift problem. Finally, the experiments in the real world demonstrate that the proposed framework outperforms the state-of-the-art baselines.
准确的交通流量预测在智能交通系统建设中起着至关重要的作用。然而，由于空间维度上的跨路网同构性以及时间维度上的周期性漂移，现有的交通流量预测方法无法很好地满足复杂的时空特征。在本文中，提出了一种用于交通流量预测的时空超图卷积网络（ST-HCN）来解决上述问题。具体而言，所提出的框架应用K均值聚类算法和物理道路网络本身的连接特性来统一局部相关性和跨路网同构性。然后，建立了一个双通道超图卷积来捕捉交通数据中的高阶空间关系。此外，所提出的框架利用带有卷积模块的长短期记忆网络（ConvLSTM）来处理周期性漂移问题。最后，在现实世界中的实验表明，所提出的框架优于当前最先进的基线方法。
