A) Short-Term (3 papers)
Z. Zhao, W. Chen, X. Wu, P. Chen, J. Liu, “LSTM Network: A Deep Learning Approach for Short‐Term Traffic Forecasting”, IET Intelligent Transport Systems, Volume 11, Issue 2, pages 68-75, March 2017.
R. Abduljabbar, H. Dia, P. Tsai, S. Liyanage, "Short-Term Traffic Forecasting: An LSTM Network for Spatial-Temporal Speed Prediction”, MDPI Future Transportation, 2021.
E. Dogan, “LSTM Training Set Analysis and Clustering Model Development for Short-Term Traffic Flow Prediction”, Neural Computing and Applications, 2021.
B) Long-Term (3 papers)
Z. Wang, X. Su, Z. Ding, "Long-Term Traffic Prediction based on LSTM Encoder-Decoder Architecture", IEEE Transactions on Intelligent Transportation Systems, Volume 22, No. 10, pages 6561-6571, October 2021.
R. Li, Y. Hu, Q. Liang, “T2F-LSTM Method for Long-Term Traffic Volume Prediction”, IEEE Transactions on Fuzzy Systems, 2020.
Y. Yang, Z. Chen, Y. Gao, Z. Wang, Z. Ding, J. Wu“Network Traffic Forecasting with Transfer Learning-based Algorithm for Long Continuous Missing Data”, Experts Systems with Applications, September 2025.
C) Attention Mechanisms (4 papers)
B. Yang, S. Sun, J. Li, X. Lin, Y. Tian, “Traffic Flow Prediction using LSTM with Feature Enhancement”, Neurocomputing, 2019.
H. Lu, Z. Ge, Y. Song, D. Jiang, T. Zhou, J. Qin, “A Temporal-Aware LSTM Enhanced by Loss-Switch Mechanism for Traffic Flow Forecasting”, Neurocomputing, 2021.
T. Zhang and G. Guo, "Graph Attention LSTM: A Spatiotemporal Approach for Traffic Flow Forecasting", IEEE Intelligent Transportation Systems Magazine, Volume 14, No. 2, pages 190-196, April 2022.
V. Chauhan, A. Tiwari, A. Kumar, “An Attention Mechanism-based Hybrid TimeAttentionBiLSTM Architecture for Long-Term Traffic Forecasting”, The Journal of Supercomputing, 2025.
D) Robustness (7 papers)
D.1) Adversarial Attacks (2 papers)
J. Jiang, B. Wu, L. Chen, K. Zhang, S. Kim, “Enhancing the Robustness Via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecasting”, ACM International Conference on Information and Knowledge Management, CIKM 2023, pages 987-996, 2023.
F. Liu, W. Zhang, H. Liu “Robust Spatiotemporal Traffic Forecasting with Reinforced Dynamic Adversarial Training”, ACM Conference on Knowledge Discovery and Data Mining, 2023, SIGKDD pages 1417-1428, 2023.
D.2) Noise Labels (2 papers)
W. Fang, W. Zhuo, Y. Song, J. Yan, T. Zhou, J. Qin, “Δfree-LSTM: An Error Distribution Free Deep Learning for Short-Term Traffic Flow Forecasting”, Neurocomputing, 2023.
L. Cai, M. Lei, S. Zhang, Y. Yu, T. Zhou, “A Noise-Immune LSTM Network for Short-Term Traffic Flow Forecasting”, AIP Publishing, 2020.
D.3) Missing Data (3 papers)
Y. Tian, K. Zhang, J. Li, X. Lin, B. Yang, “LSTM-based Traffic Flow Prediction with Missing Data”, Neurocomputing, 2018.
L. Mou, P. Zhao, H. Xie, Y. Chen, “T-LSTM: A Long Short-Term Memory Neural Network Enhanced by Temporal Information for Traffic Flow Prediction”, IEEE Access, 2019.
J. Li, F. Guo, A. Sivakumar, Y. Dong, R. Krishnan, “Transferability Improvement in Short-Term Traffic Prediction using Stacked LSTM Network”, Transportation Research Part C: Emerging Technologies, Volume 124, March 2021.