Yu, B., Yin, H. and Zhu, Z. (2017) Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. Proceedings of the 27th International Joint Conference on Artificial Intelligence, Stockholm, 13-19 July 2018, 3634-3640.
https://doi.org/10.24963/ijcai.2018/505

has been cited by the following article:

• TITLE: Multi-Head Attention Spatial-Temporal Graph Neural Networks for Traffic Forecasting

  AUTHORS: Xiuwei Hu, Enlong Yu, Xiaoyu Zhao

  KEYWORDS: Traffic Prediction, Intelligent Traffic System, Multi-Head Attention, Graph Neural Networks

  JOURNAL NAME: Journal of Computer and Communications, Vol.12 No.3, March 15, 2024

  ABSTRACT: Accurate traffic prediction is crucial for an intelligent traffic system (ITS). However, the excessive non-linearity and complexity of the spatial-temporal correlation in traffic flow severely limit the prediction accuracy of most existing models, which simply stack temporal and spatial modules and fail to capture spatial-temporal features effectively. To improve the prediction accuracy, a multi-head attention spatial-temporal graph neural network (MSTNet) is proposed in this paper. First, the traffic data is decomposed into unique time spans that conform to positive rules, and valuable traffic node attributes are mined through an adaptive graph structure. Second, time and spatial features are captured using a multi-head attention spatial-temporal module. Finally, a multi-step prediction module is used to achieve future traffic condition prediction. Numerical experiments were conducted on an open-source dataset, and the results demonstrate that MSTNet performs well in spatial-temporal feature extraction and achieves more positive forecasting results than the baseline methods.