Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Shared Spatio-temporal Attention Convolution Optimization Network for Traffic Prediction

  • Pengcheng, Li (Dept. of Computer Science and Technology, Chongqing University of Posts and Telecommunications) ;
  • Changjiu, Ke (Chongqing Customs Technology Center, Chongqing Customs District) ;
  • Hongyu, Tu (Chongqing Customs Technology Center, Chongqing Customs District) ;
  • Houbing, Zhang (Chongqing Customs Technology Center, Chongqing Customs District) ;
  • Xu, Zhang (Dept. of Computer Science and Technology, Chongqing University of Posts and Telecommunications)
  • Received : 2022.07.05
  • Accepted : 2022.12.27
  • Published : 2023.02.28
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Abstract

The traffic flow in an urban area is affected by the date, weather, and regional traffic flow. The existing methods are weak to model the dynamic road network features, which results in inadequate long-term prediction performance. To solve the problems regarding insufficient capacity for dynamic modeling of road network structures and insufficient mining of dynamic spatio-temporal features. In this study, we propose a novel traffic flow prediction framework called shared spatio-temporal attention convolution optimization network (SSTACON). The shared spatio-temporal attention convolution layer shares a spatio-temporal attention structure, that is designed to extract dynamic spatio-temporal features from historical traffic conditions. Subsequently, the graph optimization module is used to model the dynamic road network structure. The experimental evaluation conducted on two datasets shows that the proposed method outperforms state-of-the-art methods at all time intervals.

Keywords

Acknowledgement

This paper is supported by the Key Cooperation Project of Chongqing Municipal Education Commission (No. HZ2021008).

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