Multi-dimensional time-dependent dynamic graph neural network for metro passenger flow prediction

Accurate metro passenger flow prediction can provide data support for vehicle scheduling and personnel allocation by metro operation departments, ensuring the efficient utilization of related resources. In recent years, Graph Convolutional Networks (GCNs) have demonstrated excellent performance in spatial processing, making them an effective method for extracting spatiotemporal dependencies in metro passenger flow prediction. However, traditional GCN models focus solely on static relationships between stations, overlooking the dynamic changes in station relationships and typically concentrating on short-term temporal dependencies while neglecting longer-term temporal features. To fully consider the spatiotemporal relationships within the metro network, a Multi-Dimensional Temporal Dependency Graph Neural Network (MTDGNN) is proposed for metro passenger flow prediction. Specifically, 1D dilated convolutions are employed to initially extract multi-dimensional temporal dependencies, generating multiple spatiotemporal dependency extraction channels. Two correlation matrices combined with GCN are then proposed to extract spatial relationships between stations within the metro network. The extracted spatiotemporal features are further captured by a Gated Recurrent Unit (GRU) to enhance temporal feature extraction. Subsequently, a multi-head attention mechanism is utilized to integrate the extraction results from multiple channels to obtain the final prediction. Finally, the model is evaluated using metro ridership data from two cities in southwestern and central China. The results indicate that the proposed model exhibits superior predictive performance compared to other methods. The MAE values on the two datasets are 1.5% to 59.3% lower than those of other methods, and the RMSE values are 3.4% to 60.0% lower than those of other methods.