[21] Yuxuan Liang, Kun Ouyang, Yiwei Wang, Ye Liu, Junbo Zhang, Yu Zheng, and David S Rosenblum. Revisiting convolutional neural networks for citywide crowd flow analytics. In Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery, pages 578-594, 2021.
[22] Xu Liu, Yuxuan Liang, Chao Huang, Yu Zheng, Bryan Hooi, and Roger Zimmermann. When do contrastive learning signals help spatio-temporal graph forecasting? In Proceedings of the 30th International Conference on Advances in Geographic Information Systems, pages 1-12, 2022.
[23] Xu Liu, Yuxuan Liang, Chao Huang, Hengchang Hu, Yushi Cao, Bryan Hooi, and Roger Zimmermann. Do we really need graph neural networks for traffic forecasting? arXiv preprint arXiv:2301.12603, 2023.
[24] Dennis Luxen and Christian Vetter. Real-time routing with openstreetmap data. In Proceedings of the 19th ACM SIGSPATIAL international conference on advances in geographic information systems, pages 513-516, 2011.
[25] Tanwi Mallick, Prasanna Balaprakash, Eric Rask, and Jane Macfarlane. Graph-partitioning-based diffusion convolutional recurrent neural network for large-scale traffic forecasting. Transportation Research Record, pages 473-488, 2020.
[26] Eric L Manibardo, Ibai Laña, and Javier Del Ser. Deep learning for road traffic forecasting: Does it make a difference? IEEE Transactions on Intelligent Transportation Systems, pages 6164-6188, 2021.
[27] Zheyi Pan, Yuxuan Liang, Weifeng Wang, Yong Yu, Yu Zheng, and Junbo Zhang. Urban traffic prediction from spatio-temporal data using deep meta learning. In Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 1720-1730, 2019.
[28] Zezhi Shao, Zhao Zhang, Wei Wei, Fei Wang, Yongjun Xu, Xin Cao, and Christian S Jensen. Decoupled dynamic spatial-temporal graph neural network for traffic forecasting. In Proceedings of the VLDB Endowment, pages 2733-2746, 2022.
[29] David I Shuman, Sunil K Narang, Pascal Frossard, Antonio Ortega, and Pierre Vandergheynst. The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Processing Magazine, pages 83-98, 2013.
[30] Chao Song, Youfang Lin, Shengnan Guo, and Huaiyu Wan. Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 914–921, 2020.
[31] Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. Glue: A multi-task benchmark and analysis platform for natural language understanding. In International Conference on Learning Representations, 2019.
[32] Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. Graph wavenet for deep spatial-temporal graph modeling. In Proceedings of International Joint Conference on Artificial Intelligence, pages 1907-1913, 2019.
[33] Mingxing Xu, Wenrui Dai, Chunmiao Liu, Xing Gao, Weiyao Lin, Guo-Jun Qi, and Hongkai Xiong. Spatial-temporal transformer networks for traffic flow forecasting. arXiv preprint arXiv:2001.02908, 2020.
[34] Bing Yu, Haoteng Yin, and Zhanxing Zhu. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In Proceedings of International Joint Conference on Artificial Intelligence, pages 3634-3640, 2018.
[35] Yu Zheng, Licia Capra, Ouri Wolfson, and Hai Yang. Urban computing: concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology (TIST), pages 1-55, 2014.