TY  - JOUR
PY  - 2023//
TI  - Spatial-temporal hypergraph convolutional network for traffic forecasting
JO  - PeerJ Computer science
A1  - Zhao, Zhenzhen
A1  - Shen, Guojiang
A1  - Zhou, Junjie
A1  - Jin, Junchen
A1  - Kong, Xiangjie
SP  - e1450
EP  - e1450
VL  - 9
IS  - 
N2  - 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>
LA  - en
SN  - 2376-5992
UR  - http://dx.doi.org/10.7717/peerj-cs.1450
ID  - ref1
ER  -