STNN (W. Lin, China)
M. Xu, W. Dai, C Liu, X. Gao, W. Lin, G. Qi, H. Xiong, “Spatial-Temporal Transformer Networks for Traffic Flow Forecasting”, Preprint, 2021.
DSTET ()
W. Sun, Y. Jiao, J. Gao, Z. Zheng, N. Lu, "Transformer Network with Decoupled Spatial–Temporal Embedding For Traffic Flow Forecasting", Applied Intelligence, Volume 53, 2023.
STAEformer ()
H. Liu, Z. Dong, R. Jiang, J. Deng, J. Deng, Q. Chen, X. Song, "Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting”, ACM International Conference on Information and Knowledge Management, CIKM 2023, 2023.
STAMT ()
A. Liu, Y. Zhang, “An Efficient Spatial-Temporal Transformer with Temporal Aggregation and Spatial Memory for Traffic Forecasting”, Expert Systems with Applications, Volume 250, September 2024.
TLAST ()
Q. Zheng, M. Shao, Y. Zhang, "TLAST: A Time-Lag Aware Spatial-Temporal Transformer for Traffic Flow Forecasting", IEEE Transactions on Intelligent Transportation Systems, Volume 26, No. 9, pages 13144-13158, September 2025.
PatchSTG ()
Y. Fang, Y. Liang, B. Hui, Z. Shao, L. Deng, X. Liu, X. Jiang, K. Zheng,“Efficient Large-Scale Traffic Forecasting with Transformers: A Spatial Data Perspective”, ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2025, pages 307-317, 2025.
STT-SSL ()
Self-supervised Learning Frame work Based on Spatiotemporal Transformer for Long-term Traffic Forecasting
STMDFormer ()
Spatiotemporal Memory Decoupled Transformer for Traffic Flow Forecasting
STLAFormer ()
T. Cui, Y. Lu, D. Dong, C. Ren, Z. Qu, P. Li “Position-Aware Unified Embedding with Linear Attention for Distinguishable Flow Modeling”, Transportation Research Part C, 2026.
LaST (Z. Liu, China)
Z. Liu, Y. Wang, Z. Li, J. Yu, C. Wang, Z. Liu, S. Zhang, L. Xu, “LaST: A Transformer-based Network for Spatio-temporal Predictive Learning with Dynamic Local Awareness”, Knowledge-Based-Systems, March 2026.