mlsanityData to 14 Oct 2026

Multi-Graph Convolutional-Recurrent Neural Network (MGC-RNN) for Short-Term Forecasting of Transit Passenger Flow

arXiv 2107.13226 ↗First posted 28 Jul 2021cs.LGcited by 5

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Abstract (arXiv)

Short-term forecasting of passenger flow is critical for transit management and crowd regulation. Spatial dependencies, temporal dependencies, inter-station correlations driven by other latent factors, and exogenous factors bring challenges to the short-term forecasts of passenger flow of urban rail transit networks. An innovative deep learning approach, Multi-Graph Convolutional-Recurrent Neural Network (MGC-RNN) is proposed to forecast passenger flow in urban rail transit systems to incorporate these complex factors. We propose to use multiple graphs to encode the spatial and other heterogenous inter-station correlations. The temporal dynamics of the inter-station correlations are also modeled via the proposed multi-graph convolutional-recurrent neural network structure. Inflow and outflow of all stations can be collectively predicted with multiple time steps ahead via a sequence to sequence(seq2seq) architecture. The proposed method is applied to the short-term forecasts of passenger flow in Shenzhen Metro, China. The experimental results show that MGC-RNN outperforms the benchmark algorithms in terms of forecasting accuracy. Besides, it is found that the inter-station driven by network distance, network structure, and recent flow patterns are significant factors for passenger flow forecasting. Moreover, the architecture of LSTM-encoder-decoder can capture the temporal dependencies well. In general, the proposed framework could provide multiple views of passenger flow dynamics for fine prediction and exhibit a possibility for multi-source heterogeneous data fusion in the spatiotemporal forecast tasks.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 21 of its 71 references that are in the map. First posted on arXiv 28 Jul 2021.

Builds on

21 of 71 references.

References · 21

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9 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 1; 2026: 1.

5 papers.

  1. 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
  2. 2024Graph Construction with Flexible Nodes for Traffic Demand Predictioncited by 3
  3. 2023Time Series Forecasting via Semi-Asymmetric Convolutional Architecture with Global Atrous Sliding Windowcited by 0
  4. 2025Forecasting MBTA Transit Dynamics: A Performance Benchmarking of Statistical and Machine Learning Modelscited by 0
  5. 2026PING: physics informed graph neural network for advection-diffusion processes with applications to solar power forecastingcited by 0