Citation

Zhao Z, Shen G, Zhou J, Jin J, Kong X. PeerJ Comput. Sci. 2023; 9: e1450.

Copyright

(Copyright © 2023, PeerJ)

DOI

10.7717/peerj-cs.1450

PMID

37547413

PMCID

PMC10403163

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.

Language: en

Keywords

Traffic forecasting; Hypergraph convolutional network; Spatial-temporal dependencies