Forecasting network-wide multi-step metro ridership with an attention-weighted multi-view graph to sequence learning approach
作者：
Highlights：
• A multi-view graph to sequence learning method is built to predict metro ridership.
• The AW-MV-G2S model can uncover spatiotemporal dependencies between stations.
• Three different types of adjacency matrixes are designed in the developed model.
• The developed AW-MV-G2S model shows great transferability in other metro system.
• The developed AW-MV-G2S model outperforms some state-of-the-art benchmark models.
摘要
•A multi-view graph to sequence learning method is built to predict metro ridership.•The AW-MV-G2S model can uncover spatiotemporal dependencies between stations.•Three different types of adjacency matrixes are designed in the developed model.•The developed AW-MV-G2S model shows great transferability in other metro system.•The developed AW-MV-G2S model outperforms some state-of-the-art benchmark models.
论文关键词：Metro ridership prediction,Multi-step prediction,Attention mechanism,Multi-view graphs,Sequence to sequence learning
论文评审过程：Received 12 May 2022, Revised 5 August 2022, Accepted 6 August 2022, Available online 11 August 2022, Version of Record 15 August 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.118475
