Smart City Gnosys

Smart city article details

Title	Heterogeneous Augmentation Based Spatio-Temporal Graph Convolutional Network For Traffic Forecasting
ID_Doc	28936
Authors	Mao H.; Sun Z.; Qin N.
Year	2024
Published	Proceedings of the International Joint Conference on Neural Networks
DOI	http://dx.doi.org/10.1109/IJCNN60899.2024.10651254
Abstract	Accurate traffic forecasting is of great importance for Intelligent Transportation Systems (ITS) in the context of smart cities, which can address the challenging task of traffic prediction at different periods. Although current research of STGNN (Spatio-Temporal Graph Neural Networks) has advanced in modeling spatio-temporal correlations, they still face challenges in modeling the dynamic spatio-temporal correlation of traffic data due to the deficiency of static pre-defined graphs. Additionally, insufficient attention is given to the heterogeneous graph structure within spatio-temporal dependencies, despite its potential to enhance prediction accuracy. Inspired by the above two insights, we propose a novel traffic forecasting framework: Heterogeneous Augmentation Based Spatio-Temporal Graph Convolutional Network (HASTGCN). Specifically, the model employs a graph-augmentation module based on graph topology and traffic flow patterns to learn the spatio-temporal heterogeneity. Further, to simultaneously capture short-term and long-term temporal correlations, the framework incorporates a spatio-temporal block consisting of a multi-scale temporal convolution network and a spectral graph convolution network. Adequate experiments were conducted on four real-world datasets, and the experimental results demonstrate the effectiveness and superiority of our method over other baseline methods. © 2024 IEEE.
Author Keywords	graph neural networks; heterogeneity; spatio-temporal correlations; traffic forecasting

Similar Articles

Id	Similarity	Authors	Title	Published
2033	0.954	Xu J.; Li Y.; Lu W.; Wu S.; Li Y.	A Heterogeneous Traffic Spatio-Temporal Graph Convolution Model For Traffic Prediction	Physica A: Statistical Mechanics and its Applications, 641 (2024)
53044	0.938	Meng X.; Xie W.; Cui J.	Stmgfn: Spatio-Temporal Multi-Graph Fusion Network For Traffic Flow Prediction	Lecture Notes in Computer Science, 15291 LNCS (2025)
29828	0.936	Dai R.; Xu S.; Gu Q.; Ji C.; Liu K.	Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction With Navigation Data	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2020)
36944	0.936	Tian R.; Wang C.; Hu J.; Ma Z.	Mfstgn: A Multi-Scale Spatial-Temporal Fusion Graph Network For Traffic Prediction	Applied Intelligence, 53, 19 (2023)
52517	0.935	Huang X.; Pan Z.; Zhao G.	Spatial-Temporal Interactive Graph Convolutional Networks For Traffic Forecasting	2024 4th International Conference on Electronic Information Engineering and Computer Technology, EIECT 2024 (2024)
21123	0.935	Hu J.; Lin X.; Wang C.	Dstgcn: Dynamic Spatial-Temporal Graph Convolutional Network For Traffic Prediction	IEEE Sensors Journal, 22, 13 (2022)
37213	0.932	Meng X.; Xie W.; Cui J.	Mmgcrn: Multimodal And Multiview Graph Convolutional Recurrent Network For Traffic Prediction	Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE (2024)
20800	0.929	Diao Z.; Wang X.; Zhang D.; Xie G.; Chen J.; Pei C.; Meng X.; Xie K.; Zhang G.	Dmstg: Dynamic Multiview Spatio-Temporal Networks For Traffic Forecasting	IEEE Transactions on Mobile Computing, 23, 6 (2024)
52572	0.927	Tang J.; Qian T.; Liu S.; Du S.; Hu J.; Li T.	Spatio-Temporal Latent Graph Structure Learning For Traffic Forecasting	Proceedings of the International Joint Conference on Neural Networks, 2022-July (2022)
34723	0.925	Zhou J.; Shuai S.; Wang L.; Yu K.; Kong X.; Xu Z.; Shao Z.	Lane-Level Traffic Flow Prediction With Heterogeneous Data And Dynamic Graphs	Applied Sciences (Switzerland), 12, 11 (2022)