Combining knowledge graph into metro passenger flow prediction: A split-attention relational graph convolutional network
作者：
Highlights：
• This paper models the metro system as knowledge graph for passenger flow prediction.
• It combines traffic patterns and land-use features for knowledge graph construction.
• It proposes a SARGCN model for spatiotemporal prediction on metro knowledge graphs.
• It uses an attention mechanism to learn the correlation between inflow and outflow.
• Validated on two metro datasets, it outperforms numerous advanced baselines.
摘要
•This paper models the metro system as knowledge graph for passenger flow prediction.•It combines traffic patterns and land-use features for knowledge graph construction.•It proposes a SARGCN model for spatiotemporal prediction on metro knowledge graphs.•It uses an attention mechanism to learn the correlation between inflow and outflow.•Validated on two metro datasets, it outperforms numerous advanced baselines.
论文关键词：Passenger flow prediction,Urban metro system,Knowledge graph,Graph neural network,Deep learning
论文评审过程：Received 30 March 2022, Revised 25 August 2022, Accepted 4 September 2022, Available online 17 September 2022, Version of Record 26 September 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.118790
