ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2022, Vol. 59 ›› Issue (2): 282-293.doi: 10.7544/issn1000-1239.20210901

Special Issue: 2022空间数据智能专题

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Spatial-Temporal Graph Neural Network for Traffic Flow Prediction Based on Information Enhanced Transmission

Ni Qingjian1, Peng Wenqiang1, Zhang Zhizheng1, Zhai Yuqing2   

  1. 1(School of Computer Science and Engineering, Southeast University, Nanjing 211189);2(School of Cyber Science and Engineering, Southeast University, Nanjing 211189)
  • Online:2022-02-01
  • Supported by: 
    This work was supported by the National Key Research and Development Program of China (2018YFB1004300).

Abstract: Traffic problems not only affect people’s travel, but also bring environmental pollution and safety problems. Accurate traffic flow prediction is the key to build an intelligent transportation system to prevent and alleviate traffic problems. Most of the current methods do not take into account the dynamic spatial-temporal correlation, periodicity, linear and nonlinear characteristics of traffic flow. In this paper, an information enhanced transmission spatial-temporal graph neural network is proposed, which mainly contains a multi-feature attention module, an information enhanced transmission module, a temporal attention module, and a linear-nonlinear fusion module. The multi-feature attention module captures the intrinsic connection between multiple traffic features and takes into account the periodicity of traffic flow. The information enhanced transmission module makes full use of the information in the traffic network, enhancing the information transmission capability of the traffic network and mining the complex dynamic spatial dependencies. The temporal attention module adaptively extracts the dependencies between different time intervals. Furthermore, the linear-nonlinear fusion module considers both linear and nonlinear data features. A large number of comparative experiments are conducted on real-world datasets, and the experimental results demonstrate that the method proposed in this paper has more obvious advantages in terms of traffic flow prediction performance compared with the state-of-the-art baselines.

Key words: traffic flow prediction, graph neural network, spatial-temporal, information enhanced, attention

CLC Number: