Tedjopurnomo, D. A., Bao, Z., Zheng, B., Choudhury, F. M. & Qin, A. K. A survey on modern deep neural network for traffic prediction: Trends, methods and challenges. IEEE Trans. Knowl. Data Eng. 34, 1544–1561. https://doi.org/10.1109/TKDE.2020.3001195 (2022).
Isufi, E., Loukas, A., Simonetto, A. & Leus, G. Autoregressive moving average graph filtering. IEEE Trans. Signal Process. 65, 274–288. https://doi.org/10.1109/TSP.2016.2614793 (2017).
Lu, Z., Zhou, C., Wu, J., Jiang, H. & Cui, S. Integrating granger causality and vector auto-regression for traffic prediction of large-scale WLANs. KSII Trans. Internet Inf. Syst. 10, 136–151. https://doi.org/10.3837/tiis.2016.01.008 (2016).
Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A. & Vapnik, V. Support vector regression machines. In Proceedings of the 10th International Conference on Neural Information Processing Systems, NIPS’96, 155–161 (MIT Press, 1996).
Yu, B., Yin, H. & Zhu, Z. Spatio-temporal graph convolutional neural network: A deep learning framework for traffic forecasting. CoRRabs/1709.04875 arXiv:1709.04875 (2017).
Zheng, C., Fan, X., Wang, C. & Qi, J. Gman: A graph multi-attention network for traffic prediction arXiv:1911.08415 (2019).
Cui, Z., Chen, W. & Chen, Y. Multi-scale convolutional neural networks for time series classification arXiv:1603.06995 (2016).
Li, Y., Yu, R., Shahabi, C. & Liu, Y. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting arXiv:1707.01926 (2018).
Zhao, L. et al. T-GCN: A temporal graph convolutional network for traffic prediction. IEEE Trans. Intell. Transp. Syst. 21, 3848–3858. https://doi.org/10.1109/TITS.2019.2935152 (2020).
Guo, S., Lin, Y., Feng, N., Song, C. & Wan, H. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 33, 922–929 (2019).
Wang, X. et al. Traffic flow prediction via spatial temporal graph neural network. In Proceedings of The Web Conference 2020, WWW ’20, 1082–1092, https://doi.org/10.1145/3366423.3380186 (Association for Computing Machinery, 2020).
Luo, R., Song, Y., Huang, L., Zhang, Y. & Su, R. Stgin: A spatial temporal graph-informer network for long sequence traffic speed forecasting arXiv:2210.01799 (2022).
Wu, Z., Pan, S., Long, G., Jiang, J. & Zhang, C. Graph wavenet for deep spatial-temporal graph modeling arXiv:1906.00121 (2019).
Liu, Z., Shojaee, P. & Reddy, C. K. Graph-based multi-ode neural networks for spatio-temporal traffic forecasting arXiv:2305.18687 (2023).
Ghosh, B., Basu, B. & O’Mahony, M. Bayesian time-series model for short-term traffic flow forecasting. J. Transp. Eng. 133, 180–189 (2007).
Xie, Y., Zhang, Y. & Ye, Z. Short-term traffic volume forecasting using Kalman filter with discrete wavelet decomposition. Computer-Aided Civil Infrastruct. Eng. 22, 326–334 (2007).
Fu, H., Ma, H., Liu, Y. & Lu, D. A vehicle classification system based on hierarchical multi-svms in crowded traffic scenes. Neurocomputing 211, 182–190 (2016).
May, M., Hecker, D., Körner, C., Scheider, S. & Schulz, D. A vector-geometry based spatial knn-algorithm for traffic frequency predictions. In 2008 IEEE International Conference on Data Mining Workshops, 442–447, https://doi.org/10.1109/ICDMW.2008.35 (2008).
Huang, W., Song, G., Hong, H. & Xie, K. Deep architecture for traffic flow prediction: Deep belief networks with multitask learning. IEEE Trans. Intell. Transp. Syst. 15, 2191–2201. https://doi.org/10.1109/TITS.2014.2311123 (2014).
Lv, Y., Duan, Y., Kang, W., Li, Z. & Wang, F.-Y. Traffic flow prediction with big data: A deep learning approach. IEEE Trans. Intell. Transp. Syst. 16, 865–873. https://doi.org/10.1109/TITS.2014.2345663 (2015).
Fu, R., Zhang, Z. & Li, L. Using lstm and gru neural network methods for traffic flow prediction. In 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), 324–328, https://doi.org/10.1109/YAC.2016.7804912 (2016).
Sutskever, I., Vinyals, O. & Le, Q. V. Sequence to sequence learning with neural networks arXiv:1409.3215 (2014).
Wang, Y., Guo, Y., Wei, Z., Huang, Y. & Liu, X. Traffic flow prediction based on deep neural networks. In 2019 International Conference on Data Mining Workshops (ICDMW), 210–215, https://doi.org/10.1109/ICDMW.2019.00040 (2019).
Wu, Y. & Tan, H. Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework arXiv:1612.01022 (2016).
Yu, H., Wu, Z., Wang, S., Wang, Y. & Ma, X. Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks arXiv:1705.02699 (2017).
Chen, W. & Shi, K. Multi-scale attention convolutional neural network for time series classification. Neural Netw. 136, 126–140. https://doi.org/10.1016/j.neunet.2021.01.001 (2021).
Chen, Z., Ma, Q. & Lin, Z. Time-aware multi-scale rnns for time series modeling. In IJCAI, 2285–2291 (2021).
Bai, L., Yao, L., Li, C., Wang, X. & Wang, C. Adaptive graph convolutional recurrent network for traffic forecasting arXiv:2007.02842 (2020).
Li, M. et al. Traffic flow prediction with vehicle trajectories. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 35, 294–302 (2021).
Wu, Z., Pan, S., Long, G., Jiang, J. & Zhang, C. Graph wavenet for deep spatial-temporal graph modeling. arXiv:1906.00121 (2019).
Peng, H. et al. Dynamic graph convolutional network for long-term traffic flow prediction with reinforcement learning. Inf. Sci. 578, 401–416 (2021).
Wang, Y., Fang, S., Zhang, C., Xiang, S. & Pan, C. Tvgcn: Time-variant graph convolutional network for traffic forecasting. Neurocomputing 471, 118–129 (2022).
Li, C., Bai, L., Liu, W., Yao, L. & Waller, S. T. A multi-task memory network with knowledge adaptation for multimodal demand forecasting. Transp. Res. Part C Emerg. Technol. 131, 103352 (2021).
Wang, P., Zhang, T., Zheng, Y. & Hu, T. A multi-view bidirectional spatiotemporal graph network for urban traffic flow imputation. Int. J. Geogr. Inf. Sci. 36, 1231–1257 (2022).
Huang, X., Ye, Y., Yang, X. & Xiong, L. Multi-view dynamic graph convolution neural network for traffic flow prediction. Expert Syst. Appl. 222, 119779 (2023).
Vaswani, A. et al. Attention is all you need. arXiv:1706.03762 (2023).
Huang, J., Luo, K., Cao, L., Wen, Y. & Zhong, S. Learning multiaspect traffic couplings by multirelational graph attention networks for traffic prediction. IEEE Trans. Intell. Transp. Syst. 23, 20681–20695. https://doi.org/10.1109/TITS.2022.3173689 (2022).
Feng, A. & Tassiulas, L. Adaptive graph spatial-temporal transformer network for traffic flow forecasting. arXiv:2207.05064 (2022).
Jiang, J., Han, C., Zhao, W. X. & Wang, J. Pdformer: Propagation delay-aware dynamic long-range transformer for traffic flow prediction. arXiv:2301.07945 (2024).
Li, Y. et al. Ddgformer: Direction- and distance-aware graph transformer for traffic flow prediction. Knowl.-Based Syst. 302, 112381 (2024).
Berndt, D. J. & Clifford, J. Using dynamic time warping to find patterns in time series. In Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, AAAIWS’94, 359–370 (AAAI Press, 1994).
Hamilton, J. D. Time Series Analysis (Princeton University Press, 1994).
Song, C., Lin, Y., Guo, S. & Wan, H. Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 34, 914–921 (2020).
Wu, Z. et al. Connecting the dots: Multivariate time series forecasting with graph neural networks arXiv:2005.11650 (2020).
Li, M. & Zhu, Z. Spatial-temporal fusion graph neural networks for traffic flow forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35, 4189–4196 (2021).
Fang, Z., Long, Q., Song, G. & Xie, K. Spatial-temporal graph ode networks for traffic flow forecasting. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & amp; Data Mining, 364–373 (ACM, 2021).
Lan, S. et al. Dstagnn: Dynamic spatial-temporal aware graph neural network for traffic flow forecasting. In Proceedings of the 39th International Conference on Machine Learning, vol. 162 of Proceedings of Machine Learning Research (eds Chaudhuri, K. et al.) 11906–11917 (PMLR, 2022).
Xu, Y. et al. Generic dynamic graph convolutional network for traffic flow forecasting. Inf. Fusion 100, 101946 (2023).
Liu, A. & Zhang, Y. Spatial-temporal dynamic graph convolutional network with interactive learning for traffic forecasting. IEEE Trans. Intell. Transp. Syst. 25, 7645–7660. https://doi.org/10.1109/TITS.2024.3362145 (2024).
Guo, S., Lin, Y., Wan, H., Li, X. & Cong, G. Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting. IEEE Trans. Knowl. Data Eng. 34, 5415–5428. https://doi.org/10.1109/TKDE.2021.3056502 (2022).
Shao, Z., Zhang, Z., Wang, F., Wei, W. & Xu, Y. Spatial-temporal identity: A simple yet effective baseline for multivariate time series forecasting. arXiv:2208.05233 (2022).
Liu, H. et al. Staeformer: Spatio-temporal adaptive embedding makes vanilla transformer sota for traffic forecasting. arXiv:2308.10425 (2023).