Di-GraphGAN
L. Li, J. Bi, K. Yang, F. Luo, "Di-GraphGAN: An Enhanced Adversarial Learning Framework for Accurate Spatial-Temporal Traffic Forecasting under Data Missing Scenarios", Information Sciences, 2024.
TPB
Z. Liu, G. Zheng, Y. Yu,"Cross-city Few-Shot Traffic Forecasting via Traffic Pattern Bank", ACM International Conference on Information and Knowledge Management, CIKM 2023, pages 1451-1460, 2023.
MTPB
Z. Liu, G. Zheng, Y. Yu, "Multi-scale Traffic Pattern Bank for Cross-city Few-shot Traffic Forecasting", Preprint, 2024.
FEPCross
Z. Liu, J. Ding, G. Zheng, "Frequency Enhanced Pre-training for Cross-City Few-shot Traffic Forecasting", Machine Learning and Knowledge Discovery in Databases, ECML-PKDD 2024, 2024.
PDGM
X. Kong, H Wang, M. Zhang, F. Zhang, “Periodic Decomposition and Feature Enhancement Fusion for Traffic Forecasting”, Engineering Applications of Artificial Intelligence, Volume 146, April 2025.
DGIB
J. Pang, M. Wu, B. Xie, Y. Bi, Z. Luo, “Dynamic Graph Information Bottleneck for Traffic Prediction”, MDPI Electonics, February 2026.
MG-GNNs
P. Li, C. Chou, J. Tsai, H. Hsieh, “A Two-Stage Anomaly-Aware Framework for Robust Traffic Forecasting with Memory-Guided GNNs”, ACM International Conference on Web Search and Data Mining, WSDM 2026, February 2026.