Attention-based dynamic spatial-temporal graph convolutional networks for traffic speed forecasting

作者：

Highlights：

• A novel GCN-based traffic prediction model is proposed.

• The model enables dynamic spatial–temporal modeling of traffic data.

• This model has superior long-term forecasting capability.

• Hidden spatial relationships in traffic data were captured.

摘要

•A novel GCN-based traffic prediction model is proposed.•The model enables dynamic spatial–temporal modeling of traffic data.•This model has superior long-term forecasting capability.•Hidden spatial relationships in traffic data were captured.

论文关键词：Traffic speed forecast,GCN,Dynamic spatial–temporal correlations,Attention mechanism

论文评审过程：Received 12 July 2021, Revised 17 March 2022, Accepted 3 May 2022, Available online 10 May 2022, Version of Record 9 June 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117511