Dynamic and multigraph fusion spatio-temporal graph convolution network for short-term forecasting of urban rail transit passenger flow

Document Type

Journal Article

Publication Date

2026

Subject Area

place - urban, mode - rail, planning - travel demand management, ridership - modelling, ridership - forecasting

Keywords

graph convolution, multigraph fusion, short-term passenger flow prediction, urban rail transit

Abstract

Short-term passenger flow prediction is crucial for urban traffic management and crowd control. However, traditional graph neural network methods usually use static adjacency matrices in urban rail transit prediction, ignoring the dynamic changes of inter-station correlations throughout the day. To address this challenge, we propose a new model called Dynamic Multigraph Fusion Spatio-Temporal Graph Convolutional Network (DMSTGCN). This model uses Tucker decomposition to construct a time-varying dynamic graph, capturing the spatial correlations of passenger flow at different time intervals and overcoming the limitations of static matrices in previous studies while effectively mitigating overfitting. Three predefined graphs, namely the physical structure graph, the passenger flow similarity graph and the OD correlation graph, are also integrated to form the fusion graph convolution module, which further enriches the multivariate spatial correlation of passenger flows. Meanwhile, the causal temporal multi-head self-attention mechanism extracts temporal correlations, working with the fusion graph convolution module in a spatio-temporal learner for multilayer spatio-temporal feature extraction. Finally, experiments on three real-world datasets prove the excellence of DMSTGCN and offer interpretable dynamic spatial relationships.

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Permission to publish the abstract has been given by Taylor&Francis, copyright remains with them.

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