@article{ref1,
title="Spatial-temporal hypergraph convolutional network for traffic forecasting",
journal="PeerJ Computer science",
year="2023",
author="Zhao, Zhenzhen and Shen, Guojiang and Zhou, Junjie and Jin, Junchen and Kong, Xiangjie",
volume="9",
number="",
pages="e1450-e1450",
abstract="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.<p /> <p>Language: en</p>",
language="en",
issn="2376-5992",
doi="10.7717/peerj-cs.1450",
url="http://dx.doi.org/10.7717/peerj-cs.1450"
}