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Vlahogianni EI, Karlaftis MG, Golias JC. 2014. Short-term traffic forecasting: where we are and where we’re going. Transportation Research Part C: Emerging Technologies 43(3-4):3−19

Zhang X, Rice JA. 2003. Short-term travel time prediction. Transportation Research Part C: Emerging Technologies 11(3−4):187−210

Williams BM, Hoel LA. 2003. Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: theoretical basis and empirical results. Journal of Transportation Engineering 129(6):664−72

Smith BL, Williams BM, Keith Oswald R. 2002. Comparison of parametric and nonparametric models for traffic flow forecasting. Transportation Research Part C: Emerging Technologies 10(4):303−21

van Lint JWC. 2008. Online learning solutions for freeway travel time prediction. IEEE Transactions on Intelligent Transportation Systems 9:38−47

Liu X, Chien SI, Chen M. 2014. An adaptive model for highway travel time prediction. Journal of Advanced Transportation 48:642−54

de Bézenac E, Rangapuram SS, Benidis K, Bohlke-Schneider M, Kurle R, et al. 2020. Normalizing Kalman filters for multivariate time series analysis. NIPS '20: Proceedings of the 34 ^th International Conference on Neural Information Processing Systems, Vancouver, Canada, 2020. Red Hook, NY, USA: Curran Associates Inc.

Cheng YC, Li ST. 2012. Fuzzy time series forecasting with a probabilistic smoothing hidden Markov model. IEEE Transactions on Fuzzy Systems 20(2):291−304

Robinson JW, Hartemink AJ, Ghahramani Z. 2010. Learning Non-Stationary Dynamic Bayesian Networks. Journal of Machine Learning Research 11:3647−80

Zhang Y, Haghani A. 2015. A gradient boosting method to improve travel time prediction. Transportation Research Part C: Emerging Technologies 58:308−24

Castro-Neto M, Jeong YS, Jeong MK, Han LD. 2009. Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Expert Systems with Applications 36(3):6164−73

Gao P, Hu J, Zhou H, Zhang Y. 2016. Travel time prediction with immune genetic algorithm and support vector regression. 2016 12th World Congress on Intelligent Control and Automation (WCICA), Guilin, China, 12-15 June 2016. USA: IEEE. pp. 987−92. doi: 10.1109/WCICA.2016.7578434

Zhao J, Gao Y, Tang J, Zhu L, Ma J. 2018. Highway travel time prediction using sparse tensor completion tactics and K-nearest neighbor pattern matching method. Journal of Advanced Transportation 2018:5721058

Chiabaut N, Faitout R. 2021. Traffic congestion and travel time prediction based on historical congestion maps and identification of consensual days. Transportation Research Part C: Emerging Technologies 124:102920

Kwak S, Geroliminis N. 2021. Travel time prediction for congested freeways with a dynamic linear model. IEEE Transactions on Intelligent Transportation Systems 22:7667−77

Lv Y, Duan Y, Kang W, Li Z, Wang FY. 2015. Traffic flow prediction with big data: a deep learning approach. IEEE Transactions on Intelligent Transportation Systems 16(2):865−73

Yang G, Wang Y, Yu H, Ren Y, Xie J. 2018. Short-term traffic state prediction based on the spatiotemporal features of critical road sections. Sensors 18(7):2287

Yao H, Wu F, Ke J, Tang X, Jia Y, et al. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. Proceedings of the AAAI Conference on Artificial Intelligence 32:2588−95

Cui Z, Ke R, Pu Z, Wang Y. 2020. Stacked bidirectional and unidirectional LSTM recurrent neural network for forecasting network-wide traffic state with missing values. Transportation Research Part C: Emerging Technologies 118:102674

Ran X, Shan Z, Fang Y, Lin C. 2019. An LSTM-based method with attention mechanism for travel time prediction. Sensors 19(4):861

Li Y, Yu R, Shahabi C, Liu Y. 2018. Diffusion convolutional recurrent neural network: data-driven traffic forecasting. arXiv Preprint

Wu Z, Pan S, Long G, Jiang J, Zhang C. 2019. Graph WaveNet for deep spatial-temporal graph modeling. arXiv Preprint

Guo S, Lin Y, Feng N, Song C, Wan H. 2019. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 33:922−29

Yu JJQ, Gu J. 2019. Real-time traffic speed estimation with graph convolutional generative autoencoder. IEEE Transactions on Intelligent Transportation Systems 20(10):3940−51

Song C, Lin Y, Guo S, Wan H. 2020. Spatial-temporal synchronous graph convolutional networks: a new framework for spatial-temporal network data forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 34:914−21

Huang R, Huang C, Liu Y, Dai G, Kong W. 2020. LSGCN: Long short-term traffic prediction with graph convolutional networks. IJCAI 7:2355−61

Zheng C, Fan X, Wang C, Qi J. 2020. GMAN: a graph multi-attention network for traffic prediction. Proceedings of the AAAI Conference on Artificial Intelligence 34:1234−41

Liu X, Liang Y, Huang C, Hu H, Cao Y, et al. 2023. Do we really need graph neural networks for traffic forecasting? arXiv Preprint

Kong W, Guo Z, Liu Y. 2024. Spatio-temporal pivotal graph neural networks for traffic flow forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 38(8):8627−35

Wu Y, Tan H, Qin L, Ran B, Jiang Z. 2018. A hybrid deep learning based traffic flow prediction method and its understanding. Transportation Research Part C: Emerging Technologies 90:166−80

Song J, Son J, Seo DH, Han K, Kim N, et al. 2022. ST-GAT: a spatio-temporal graph attention network for accurate traffic speed prediction. CIKM '22: Proceedings of the 31 ^st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA, October 17−21, 2022. New York, USA:Association for Computing Machinery. pp. 4500−4. doi: 10.1145/3511808.3557705

Gupta M, Kodamana H, Ranu S. 2023. Frigate: Frugal spatio-temporal forecasting on road networks. KDD '23: Proceedings of the 29 ^th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Long Beach, USA , August 6−10, 2023. New York, USA: Association for Computing Machinery. pp. 649−60. doi: 10.1145/3580305.3599357

Yan H, Ma X, Pu Z. 2022. Learning dynamic and hierarchical traffic spatiotemporal features with transformer. IEEE Transactions on Intelligent Transportation Systems 23(11):22386−99

Cao H, Huang Z, Yao T, Wang J, He H, et al. 2023. InParformer: evolutionary decomposition transformers with interactive parallel attention for long-term time series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 37(6):6906−15

Li Z, Xia L, Xu Y, Huang C. 2023. GPT-ST: generative pre-training of spatio-temporal graph neural networks. arXiv Preprint

Liu F, Zhang W, Liu H. 2023. Robust spatiotemporal traffic forecasting with reinforced dynamic adversarial training. KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Long Beach, CA, USA, August 6−10, 2023. New York, USA: Association for Computing Machinery. pp. 1417−28. https://doi.org/10.1145/3580305.3599492

Yao C, Li Z, Wang J. 2023. Spatio-temporal hypergraph neural ODE network for traffic forecasting. 2023 IEEE International Conference on Data Mining (ICDM), Shanghai, China, 1−4 December 2023. USA: IEEE. pp. 1499−504. doi: 10.1109/ICDM58522.2023.00198

Fang Z, Long Q, Song G, Xie K. 2021. Spatial-temporal graph ODE networks for traffic flow forecasting. KDD '21: Proceedings of the 27 ^th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. Virtual Event, Singapore, August 14−18, 2021. New York, USA: Association for Computing Machinery. pp. 364−37. doi: 10.1145/3447548.3467430

Jiang J, Wu B, Chen L, Zhang K, Kim S. 2023. Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecasting. Proceedings of the 32 ^nd ACM International Conference on Information and Knowledge Management. Birmingham, United Kingdom, October 21−25, 2023. New York, USA: Association for Computing Machinery. pp. 987−96. doi: 10.1145/3583780.3614868

Chung J, Ahn S, Bengio Y. 2016. Hierarchical multiscale recurrent neural networks. arXiv Preprint

Pan P, Xu Z, Yang Y, Wu F, Zhuang Y. 2016. Hierarchical recurrent neural encoder for video representation with application to captioning. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27−30 June 2016. USA: IEEE. pp. 1029−38. doi: 10.1109/CVPR.2016.117

Lin J, Gan C, Han S. 2019. TSM: temporal shift module for efficient video understanding. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South), 27 October − 2 November 2019. USA: IEEE. pp. 7082−92. doi: 10.1109/ICCV.2019.00718

Ramaswamy A, Seemakurthy K, Gubbi J, Purushothaman B. 2020. Spatio-temporal action detection and localization using a hierarchical LSTM. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA, 14−19 June 2020. USA: IEEE. pp. 3303−12. doi: 10.1109/CVPRW50498.2020.00390

Liu Y, Ponce C, Brunton SL, Kutz JN. 2023. Multiresolution convolutional autoencoders. Journal of Computational Physics 474:111801

Chu KF, Lam AYS, Li VOK. 2018. Travel demand prediction using deep multi-scale convolutional LSTM network. 2018 21 ^st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA, 4−7 November 2018. USA: IEEE. pp 1402−7. doi: 10.1109/ITSC.2018.8569427

Lin J, Zhong SH, Fares A. 2022. Deep hierarchical LSTM networks with attention for video summarization. Computers & Electrical Engineering 97:107618

Guo S, Guo WG, Bain L. 2020. Hierarchical spatial-temporal modeling and monitoring of melt pool evolution in laser-based additive manufacturing. IISE Transactions 52(9):977−97

Ma Q, Zhang Z, Zhao X, Li H, Zhao H, et al. 2023. Rethinking sensors modeling: hierarchical information enhanced traffic forecasting. CIKM '23: Proceedings of the 32 ^nd ACM International Conference on Information and Knowledge Management, Birmingham, United Kingdom, October 21−25, 2023. New York, USA: Association for Computing Machinery. pp. 1756−65. doi: 10.1145/3583780.36149

Shi X, Chen Z, Wang H, Yeung DY, Wong WK, et al. 2015. Convolutional LSTM network: a machine learning approach for precipitation nowcasting. Advances in Neural Information Processing Systems 2015:802−10

Kalchbrenner N, Danihelka I, Graves A. 2015. Grid long short-term memory. arXiv 1507:01526

Wang Y, Jiang L, Yang MH, Li LJ, Long M, et al. 2019. Eidetic 3d LSTM: A model for video prediction and beyond

Liu X, Xia Y, Liang Y, Hu J, Wang Y, et al. 2023. LargeST: a benchmark dataset for large-scale traffic forecasting. arXiv Preprint

Wang J, Jiang J, Jiang W, Li C, Zhao WX. 2021. LibCity: an open library for traffic prediction. SIGSPATIAL '21: Proceedings of the 29 ^th International Conference on Advances in Geographic Information Systems, Beijing, China, 2−5 November 2021. New York, USA: Association for Computing Machinery. pp. 145−48. doi: 10.1145/3474717.3483923

Wu Z, Pan S, Long G, Jiang J, Chang X, et al. 2020. Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks. KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual Event CA, USA, 6−10 July 2020. New York, USA: Association for Computing Machinery. pp. 753−63. doi: 10.1145/3394486.3403118

Bai L, Yao L, Li C, Wang X, Wang C. 2020. Adaptive graph convolutional recurrent network for traffic forecasting. NIPS '20: Proceedings of the 34 ^th International Conference on Neural Information Processing Systems, Vancouver, BC, Canada, 2020. Red Hook, NY, USA: Curran Associates Inc.

Yu B, Yin H, Zhu Z. 2018. Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. IJCAI'18: Proceedings of the 27 ^th International Joint Conference on Artificial Intelligence, Stockholm, Sweden, 2018. Menlo Park, USA: AAAI Press. pp. 3634−40.

Fu R, Zhang Z, Li L. 2016. Using LSTM and GRU neural network methods for traffic flow prediction. 2016 31 ^st Youth Academic Annual Conference of Chinese Association of Automation (YAC), Wuhan, China, 11−13 November 2016. USA: IEEE. pp. 324−28. doi: 10.1109/YAC.2016.7804912

Sutskever I, Vinyals O, Le QV. 2014. Sequence to sequence learning with neural networks. Advances in Neural Information Processing Systems 4:3104−12

Wang W, Huang Y, Wang Y, Wang L. 2014. Generalized autoencoder: a neural network framework for dimensionality reduction. 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Columbus, OH, USA, 23−28 June 2014. UAS: IEEE. pp. 496−503. doi: 10.1109/CVPRW.2014.79

Bai L, Yao L, Kanhere SS, Wang X, Sheng QZ. 2019. STG2Seq: spatial-temporal graph to sequence model for multi-step passenger demand forecasting. arXiv Preprint

Zhao L, Song Y, Zhang C, Liu Y, Wang P, et al. 2019. T-GCN: a temporal graph convolutional network for traffic prediction. IEEE Transactions on Intelligent Transportation Systems 21:3848−58