mlsanityData to 14 Oct 2026

Parallel Multi-Graph Convolution Network For Metro Passenger Volume Prediction

arXiv 2109.00924 ↗First posted 29 Aug 2021cs.LGcited by 2

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Abstract (arXiv)

Accurate prediction of metro passenger volume (number of passengers) is valuable to realize real-time metro system management, which is a pivotal yet challenging task in intelligent transportation. Due to the complex spatial correlation and temporal variation of urban subway ridership behavior, deep learning has been widely used to capture non-linear spatial-temporal dependencies. Unfortunately, the current deep learning methods only adopt graph convolutional network as a component to model spatial relationship, without making full use of the different spatial correlation patterns between stations. In order to further improve the accuracy of metro passenger volume prediction, a deep learning model composed of Parallel multi-graph convolution and stacked Bidirectional unidirectional Gated Recurrent Unit (PB-GRU) was proposed in this paper. The parallel multi-graph convolution captures the origin-destination (OD) distribution and similar flow pattern between the metro stations, while bidirectional gated recurrent unit considers the passenger volume sequence in forward and backward directions and learns complex temporal features. Extensive experiments on two real-world datasets of subway passenger flow show the efficacy of the model. Surprisingly, compared with the existing methods, PB-GRU achieves much lower prediction error.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 19 of its 28 references that are in the map. First posted on arXiv 29 Aug 2021.

Builds on

19 of 28 references.

References · 19

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7 more
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Citing papers per year: 2022: 2.

2 papers.

  1. 2022Convolutional Learning on Multigraphscited by 10
  2. 2022Learning with Multigraph Convolutional Filterscited by 5