Yifei Ren, Jinjin Tang, Changhao Ying, Qiuhan Dong
Short-term origin–destination (OD) prediction for urban rail transit is critical for efficient operation and dynamic management. Unlike conventional passenger flow forecasts that focus solely on aggregate indicators such as entries and exits, OD prediction reconstructs the complete station-to-station flow matrix, enabling fine-grained operational planning and providing a stronger basis for service design, transfer coordination, and capacity allocation from a network-wide demand perspective. However, the high dimensionality and sparsity of the OD matrix, along with complex spatiotemporal dependencies, make this task challenging. This paper introduces multi-dimensional holiday and weather tags, which are coupled with historical OD tensors into a unified multi-dimensional tensor input. The dual-graph spatial encoder captures the physical network topology and latent functional relationships between stations, while the hierarchical time-series Transformer models the short-term dynamics and periodic patterns. Furthermore, we integrate Tucker tensor decomposition in the decoder to efficiently reconstruct the OD prediction results and address data sparsity. Experiments on large-scale urban rail transit datasets show that Spatio-Temporal Graph Transformer Network (STGTN) significantly outperforms benchmark methods in prediction accuracy, with a 58% reduction in Mean Absolute Error (MAE) and an 11% increase in accuracy. Larger relative errors are mainly concentrated in peripheral or infrequently used OD pairs, whereas major travel corridors are predicted accurately. Ablation studies further confirm the contribution of each module and the external factors. These results demonstrate the effectiveness of tensor-based spatiotemporal modeling and highlight the practical value of OD prediction for short-term capacity deployment, transfer coordination, and congestion mitigation in urban rail transit systems.