Fetching the paper…
Reading the bibliography…
Urban spatio-temporal flow prediction, encompassing traffic flows and crowd flows, is crucial for optimizing city infrastructure and managing traffic and emergency responses.
Z. Wu, S. Pan, G. Long, J. Jiang, and C. Zhang, “Graph wavenet for deep spatial-temporal graph modeling,” in Proceedings of the 28th International Joint Conference on Artificial Intelligence , 2019, pp. 1907–1913
1913
Earlier work this paper cites.
G. Karypis and V. Kumar, “Metis: A software package for partitioning unstructured graphs, partitioning meshes, and computing fill-reducing orderings of sparse matrices,” 1997
1997
Earlier work this paper cites.
Y. Zheng, L. Capra, O. Wolfson, and H. Yang, “Urban computing: concepts, methodologies, and applications,” ACM Transactions on Intelligent Systems and Technology (TIST) , vol. 5, no. 3, pp. 1–55, 2014
2014
Earlier work this paper cites.
J. Zhang, Y. Zheng, and D. Qi, “Deep spatio-temporal residual networks for citywide crowd flows prediction,” in Proceedings of the AAAI conference on artificial intelligence , vol. 31, no. 1, 2017
2017
Earlier work this paper cites.
Y. Wang, M. Long, J. Wang, Z. Gao, and P. S. Yu, “Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
B. Yu, H. Yin, and Z. Zhu, “Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting,” in Proceedings of the 27th International Joint Conference on Artificial Intelligence , 2018, pp. 3634–3640
2018
Earlier work this paper cites.
Y. Li, R. Yu, C. Shahabi, and Y. Liu, “Diffusion convolutional recurrent neural network: Data-driven traffic forecasting,” in International Conference on Learning Representations , 2018
2018
Earlier work this paper cites.
L. Liu, R. Zhang, J. Peng, G. Li, B. Du, and L. Lin, “Attentive crowd flow machines,” in Proceedings of the 26th ACM international conference on Multimedia , 2018, pp. 1553–1561
2018
Earlier work this paper cites.
Y. Liang, S. Ke, J. Zhang, X. Yi, and Y. Zheng, “Geoman: Multi-level attention networks for geo-sensory time series prediction.” in IJCAI , vol. 2018, 2018, pp. 3428–3434
2018
Earlier work this paper cites.
Y. Wang, Z. Gao, M. Long, J. Wang, and S. Y. Philip, “Predrnn++: Towards a resolution of the deep-in-time dilemma in spatiotemporal predictive learning,” in International Conference on Machine Learning . PMLR, 2018, pp. 5123–5132
2018
Earlier work this paper cites.
X. Geng, Y. Li, L. Wang, L. Zhang, Q. Yang, J. Ye, and Y. Liu, “Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting,” in Proceedings of the AAAI conference on artificial intelligence , vol. 33, no. 01, 2019, pp. 3656–3663
2019
Earlier work this paper cites.
Y. Liang, K. Ouyang, L. Jing, S. Ruan, Y. Liu, J. Zhang, D. S. Rosenblum, and Y. Zheng, “Urbanfm: Inferring fine-grained urban flows,” in Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining , 2019, pp. 3132–3142
2019
Earlier work this paper cites.
Z. Pan, Y. Liang, W. Wang, Y. Yu, Y. Zheng, and J. Zhang, “Urban traffic prediction from spatio-temporal data using deep meta learning,” in Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining , 2019, pp. 1720–1730
2019
Earlier work this paper cites.
L. Zhao, Y. Song, C. Zhang, Y. Liu, P. Wang, T. Lin, M. Deng, and H. Li, “T-gcn: A temporal graph convolutional network for traffic prediction,” IEEE transactions on intelligent transportation systems , vol. 21, no. 9, pp. 3848–3858, 2019
2019
Earlier work this paper cites.
Y. Wang, J. Zhang, H. Zhu, M. Long, J. Wang, and P. S. Yu, “Memory in memory: A predictive neural network for learning higher-order non-stationarity from spatiotemporal dynamics,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 9154–9162
2019
Earlier work this paper cites.
S. Wang, J. Cao, and S. Y. Philip, “Deep learning for spatio-temporal data mining: A survey,” IEEE transactions on knowledge and data engineering , vol. 34, no. 8, pp. 3681–3700, 2020
2020
Earlier work this paper cites.
L. Bai, L. Yao, C. Li, X. Wang, and C. Wang, “Adaptive graph convolutional recurrent network for traffic forecasting,” Advances in neural information processing systems , vol. 33, pp. 17 804–17 815, 2020
2020
Earlier work this paper cites.
C. Yu, X. Ma, J. Ren, H. Zhao, and S. Yi, “Spatio-temporal graph transformer networks for pedestrian trajectory prediction,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XII 16 . Springer, 2020, pp. 507–523
2020
Earlier work this paper cites.
P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W.-t. Yih, T. Rocktäschel et al. , “Retrieval-augmented generation for knowledge-intensive nlp tasks,” Advances in Neural Information Processing Systems , vol. 33, pp. 9459–9474, 2020
2020
Earlier work this paper cites.
Z. Lin, M. Li, Z. Zheng, Y. Cheng, and C. Yuan, “Self-attention convlstm for spatiotemporal prediction,” in Proceedings of the AAAI conference on artificial intelligence , vol. 34, no. 07, 2020, pp. 11 531–11 538
2020
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
Earlier work this paper cites.
Z. Wu, S. Pan, G. Long, J. Jiang, X. Chang, and C. Zhang, “Connecting the dots: Multivariate time series forecasting with graph neural networks,” in Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining , 2020, pp. 753–763
2020
Cited alongside, same era.
W. Chen, F. Wang, and H. Sun, “S2tnet: Spatio-temporal transformer networks for trajectory prediction in autonomous driving,” in Asian Conference on Machine Learning . PMLR, 2021, pp. 454–469
2021
Cited alongside, same era.
K. Jin, J. Wi, E. Lee, S. Kang, S. Kim, and Y. Kim, “Trafficbert: Pre-trained model with large-scale data for long-range traffic flow forecasting,” Expert Systems with Applications , vol. 186, p. 115738, 2021
2021
Cited alongside, same era.
J. Deng, X. Chen, R. Jiang, X. Song, and I. W. Tsang, “St-norm: Spatial and temporal normalization for multi-variate time series forecasting,” in Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining , 2021, pp. 269–278
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
Cited alongside, same era.
Z. Chang, X. Zhang, S. Wang, S. Ma, Y. Ye, X. Xinguang, and W. Gao, “Mau: A motion-aware unit for video prediction and beyond,” Advances in Neural Information Processing Systems , vol. 34, pp. 26 950–26 962, 2021
2021
Cited alongside, same era.
C. Shang and J. Chen, “Discrete graph structure learning for forecasting multiple time series,” in Proceedings of International Conference on Learning Representations , 2021
2021
Cited alongside, same era.
C. Chen, Y. Liu, L. Chen, and C. Zhang, “Bidirectional spatial-temporal adaptive transformer for urban traffic flow forecasting,” IEEE Transactions on Neural Networks and Learning Systems , 2022
2022
Cited alongside, same era.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 10 684–10 695
2022
Cited alongside, same era.
Z. Shao, Z. Zhang, F. Wang, and Y. Xu, “Pre-training enhanced spatial-temporal graph neural network for multivariate time series forecasting,” in KDD ’22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022 . ACM, 2022, pp. 1567–1577
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Z. Shao, Z. Zhang, F. Wang, W. Wei, and Y. Xu, “Spatial-temporal identity: A simple yet effective baseline for multivariate time series forecasting,” in Proceedings of the 31st ACM International Conference on Information & Knowledge Management , 2022, pp. 4454–4458
2022
Cited alongside, same era.
L. Zhao, M. Gao, and Z. Wang, “St-gsp: Spatial-temporal global semantic representation learning for urban flow prediction,” in Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining , 2022, pp. 1443–1451
2022
Cited alongside, same era.
T. Zhou, P. Niu, X. Wang, L. Sun, and R. Jin, “One fits all: Power general time series analysis by pretrained LM,” in Thirty-seventh Conference on Neural Information Processing Systems , 2023. [Online]. Available: https://openreview.net/forum?id=gMS6FVZvmF
2023
Later among the works it cites.
Z. Li, L. Xia, Y. Xu, and C. Huang, “Generative pre-training of spatio-temporal graph neural networks,” in Thirty-seventh Conference on Neural Information Processing Systems , 2023. [Online]. Available: https://openreview.net/forum?id=nMH5cUaSj8
2023
Later among the works it cites.
J. Ji, J. Wang, C. Huang, J. Wu, B. Xu, Z. Wu, Z. Junbo, and Y. Zheng, “Spatio-temporal self-supervised learning for traffic flow prediction,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 4, pp. 4356–4364, 2023
2023
Later among the works it cites.
Z. Zhang, X. Zhao, Q. Liu, C. Zhang, Q. Ma, W. Wang, H. Zhao, Y. Wang, and Z. Liu, “Promptst: Prompt-enhanced spatio-temporal multi-attribute prediction,” in Proceedings of the 32nd ACM International Conference on Information and Knowledge Management , 2023, pp. 3195–3205
2023
Later among the works it cites.
2023
Later among the works it cites.
X. Zhang, Y. Gong, X. Zhang, X. Wu, C. Zhang, and X. Dong, “Mask-and contrast-enhanced spatio-temporal learning for urban flow prediction,” in Proceedings of the 32nd ACM International Conference on Information and Knowledge Management , 2023, pp. 3298–3307
2023
Later among the works it cites.
C. Tan, Z. Gao, L. Wu, Y. Xu, J. Xia, S. Li, and S. Z. Li, “Temporal attention unit: Towards efficient spatiotemporal predictive learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 18 770–18 782
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Yuan, C. Shao, J. Ding, D. Jin, and Y. Li, “Spatio-temporal few-shot learning via diffusive neural network generation,” in The Twelfth International Conference on Learning Representations , 2024. [Online]. Available: https://openreview.net/forum?id=QyFm3D3Tzi
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
Z. Li, L. Xia, J. Tang, Y. Xu, L. Shi, L. Xia, D. Yin, and C. Huang, “Urbangpt: Spatio-temporal large language models,” 2024
2024
Closest in time.
Y. Yuan, J. Ding, J. Feng, D. Jin, and Y. Li, “Unist: a prompt-empowered universal model for urban spatio-temporal prediction,” in Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2024, pp. 4095–4106
2024
Closest in time.
L. Liu, S. Yu, R. Wang, Z. Ma, and Y. Shen, “How can large language models understand spatial-temporal data?” 2024
2024
Closest in time.
X. Cheng, D. Luo, X. Chen, L. Liu, D. Zhao, and R. Yan, “Lift yourself up: Retrieval-augmented text generation with self-memory,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
J. Lin, R. Shan, C. Zhu, K. Du, B. Chen, S. Quan, R. Tang, Y. Yu, and W. Zhang, “Rella: Retrieval-enhanced large language models for lifelong sequential behavior comprehension in recommendation,” in Proceedings of the ACM on Web Conference 2024 , 2024, pp. 3497–3508
2024
Closest in time.