SPATIOTEMPORAL GRAPH CONVOLUTIONAL NEURAL NETWORKS FOR METRO FLOW PREDICTION

8 citations as recorded by crossref.

1. Learning from leading indicators to predict long-term dynamics of hourly electricity generation from multiple resources Z. Wang et al. https://doi.org/10.1016/j.neunet.2025.107268
2. Portfolio Management Framework for Autonomous Stock Selection and Allocation J. Kim et al. https://doi.org/10.1109/ACCESS.2022.3231889
3. Graph Neural Networks for Intelligent Transportation Systems: A Survey S. Rahmani et al. https://doi.org/10.1109/TITS.2023.3257759
4. A review of research methods on the coupling relationship between urban rail transit and urban space: revealing spatiotemporal relationships through big data Z. Liu et al. https://doi.org/10.1080/17538947.2024.2339363
5. DGTNet:dynamic graph attention transformer network for traffic flow forecasting J. Chen et al. https://doi.org/10.1088/2631-8695/ad9238
6. A Systematic Review of Graph Neural Network for Node Clustering in Homogeneous Attributed Networks G. Messias et al. https://doi.org/10.1109/ACCESS.2025.3646597
7. Metro passenger flow prediction based on multi-view hypergraph spatio-temporal attention convolution X. Li et al. https://doi.org/10.1016/j.knosys.2026.116905
8. Diversified Adaptive Stock Selection Using Continual Graph Learning and Ensemble Approach J. Kim et al. https://doi.org/10.1109/ACCESS.2023.3347608