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Traffic prediction, an essential component for intelligent transportation systems, endeavours to use historical data to foresee future traffic features at specific locations.
Z. Wu, S. Pan, G. Long, J. Jiang, and C. Zhang, “Graph wavenet for deep spatial-temporal graph modeling,” in IJCAI , 2019, pp. 1907–1913
1913
Earlier work this paper cites.
S. V. Kumar and L. Vanajakshi, “Short-term traffic flow prediction using seasonal arima model with limited input data,” European Transport Research Review , vol. 7, no. 3, pp. 1–9, 2015
2015
Earlier work this paper cites.
Z. Yuan, X. Zhou, and T. Yang, “Hetero-convlstm: A deep learning approach to traffic accident prediction on heterogeneous spatio-temporal data,” in SIGKDD , 2018, pp. 984–992
2018
Earlier work this paper cites.
B. Shen, X. Liang, Y. Ouyang, M. Liu, W. Zheng, and K. M. Carley, “Stepdeep: A novel spatial-temporal mobility event prediction framework based on deep neural network,” in SIGKDD , 2018, pp. 724–733
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 ICLR , 2018, pp. 1–16
2018
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 IJCAI , 2018, p. 3634–3640
2018
Earlier work this paper cites.
A. Radford, K. Narasimhan, T. Salimans, I. Sutskever et al. , “Improving language understanding by generative pre-training,” OpenAI blog , 2018
2018
Earlier work this paper cites.
S. Guo, Y. Lin, N. Feng, C. Song, and H. Wan, “Attention based spatial-temporal graph convolutional networks for traffic flow forecasting,” in AAAI , 2019, pp. 922–929
2019
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
2019
Earlier work this paper cites.
S. Wang, H. Miao, H. Chen, and Z. Huang, “Multi-task adversarial spatial-temporal networks for crowd flow prediction,” in CIKM , 2020, p. 1555–1564
2020
Earlier work this paper cites.
H. Chen, D. Wang, and C. Liu, “Towards semantic travel behavior prediction for private car users,” in HPCC , 2020, pp. 950–957
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,” NeurIPS , vol. 33, pp. 17 804–17 815, 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 AAAI , 2020, pp. 11 531–11 538
2020
Earlier work this paper cites.
C. Zheng, X. Fan, C. Wang, and J. Qi, “Gman: A graph multi-attention network for traffic prediction,” in AAAI , vol. 34, no. 01, 2020, pp. 1234–1241
2020
Earlier work this paper cites.
C. Song, Y. Lin, S. Guo, and H. Wan, “Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting,” in AAAI , vol. 34, no. 01, 2020, pp. 914–921
2020
Earlier work this paper cites.
C. Liu, J. Cai, D. Wang, J. Tang, L. Wang, H. Chen, and Z. Xiao, “Understanding the regular travel behavior of private vehicles: An empirical evaluation and a semi-supervised model,” IEEE Sensors Journal , vol. 21, no. 17, pp. 19 078–19 090, 2021
2021
Earlier work this paper cites.
C. Liu, D. Wang, H. Chen, and R. Li, “Study of forecasting urban private car volumes based on multi-source heterogeneous data fusion.” Journal on Communication , vol. 42, no. 3, 2021
2021
Earlier work this paper cites.
X. Yin, G. Wu, J. Wei, Y. Shen, H. Qi, and B. Yin, “Deep learning on traffic prediction: Methods, analysis, and future directions,” IEEE Trans. Intell. Transp. Syst. , vol. 23, no. 6, pp. 4927–4943, 2021
2021
Earlier work this paper cites.
J. Ye, L. Sun, B. Du, Y. Fu, and H. Xiong, “Coupled layer-wise graph convolution for transportation demand prediction,” in AAAI , 2021, pp. 4617–4625
2021
Earlier work this paper cites.
J. Xiao, Z. Xiao, D. Wang, V. Havyarimana, C. Liu, C. Zou, and D. Wu, “Vehicle trajectory interpolation based on ensemble transfer regression,” IEEE Trans. Intell. Transp. Syst. , vol. 23, no. 7, pp. 7680–7691, 2022
2022
Earlier work this paper cites.
Q. Xu, S. Ruan, C. Long, L. Yu, and C. Zhang, “Traffic speed imputation with spatio-temporal attentions and cycle-perceptual training,” in CIKM , 2022, pp. 2280–2289
2022
Cited alongside, same era.
G. Jin, C. Liu, Z. Xi, H. Sha, Y. Liu, and J. Huang, “Adaptive dual-view wavenet for urban spatial-temporal event prediction,” Inf. Sci. , vol. 588, pp. 315–330, 2022
2022
Cited alongside, same era.
S. Guo, Y. Lin, H. Wan, X. Li, and G. Cong, “Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting,” IEEE Trans. Knowl. Data Eng. , vol. 34, no. 11, pp. 5415–5428, 2022
2022
Cited alongside, same era.
H. Xue, B. P. Voutharoja, and F. D. Salim, “Leveraging language foundation models for human mobility forecasting,” in SIGSPATIAL , 2022, pp. 1–9
2022
Cited alongside, same era.
Z. Yu, S. Wu, Y. Fu, S. Zhang, and Y. C. Lin, “Hint-aug: Drawing hints from foundation vision transformers towards boosted few-shot parameter-efficient tuning,” in CVPR , 2023, pp. 11 102–11 112
2023
Later among the works it cites.
A. Ramezani and Y. Xu, “Knowledge of cultural moral norms in large language models,” in ACL , 2023, pp. 428–446
2023
Later among the works it cites.
J. Maynez, P. Agrawal, and S. Gehrmann, “Benchmarking large language model capabilities for conditional generation,” in ACL , 2023, pp. 9194–9213
2023
Later among the works it cites.
H. Xue and F. D. Salim, “Promptcast: A new prompt-based learning paradigm for time series forecasting,” IEEE Trans. Knowl. Data Eng. , pp. 1–14, 2023
2023
Later among the works it cites.
D. Cao, F. Jia, S. O. Arik, T. Pfister, Y. Zheng, W. Ye, and Y. Liu, “Tempo: Prompt-based generative pre-trained transformer for time series forecasting,” in ICLR , 2023
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2022
Cited alongside, same era.
J. Choi, H. Choi, J. Hwang, and N. Park, “Graph neural controlled differential equations for traffic forecasting,” in AAAI , 2022
2022
Cited alongside, same era.
H. Xue, F. D. Salim, Y. Ren, and C. L. Clarke, “Translating human mobility forecasting through natural language generation,” in WSDM , 2022, pp. 1224–1233
2022
Cited alongside, same era.
K. Lu, A. Grover, P. Abbeel, and I. Mordatch, “Frozen pretrained transformers as universal computation engines,” in AAAI , 2022, pp. 7628–7636
2022
Cited alongside, same era.
J. Jiang, C. Han, W. X. Zhao, and J. Wang, “Pdformer: Propagation delay-aware dynamic long-range transformer for traffic flow prediction,” in AAAI , 2023, pp. 4365–4373
2023
Cited alongside, same era.
S. Y. Chang, H.-C. Wu, and Y.-C. Kao, “Tensor extended kalman filter and its application to traffic prediction,” IEEE Trans. Intell. Transp. Syst. , vol. 24, no. 12, pp. 13 813–13 829, 2023
2023
Cited alongside, same era.
F. Li, J. Feng, H. Yan, G. Jin, F. Yang, F. Sun, D. Jin, and Y. Li, “Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution,” ACM Trans. Knowl. Discov. Data , vol. 17, no. 1, pp. 9:1–9:21, 2023
2023
Cited alongside, same era.
J. Gong, Y. Liu, T. Li, H. Chai, X. Wang, J. Feng, C. Deng, D. Jin, and Y. Li, “Empowering spatial knowledge graph for mobile traffic prediction,” in SIGSPATIAL , 2023, pp. 1–11
2023
Cited alongside, same era.
2023
Later among the works it cites.
T. Zhou, P. Niu, X. Wang, L. Sun, and R. Jin, “One Fits All: Power general time series analysis by pretrained lm,” in NeurIPS , 2023, pp. 1–34
2023
Later among the works it cites.
M. Lablack and Y. Shen, “Spatio-temporal graph mixformer for traffic forecasting,” Expert Systems with Applications , vol. 228, p. 120281, 2023
2023
Later among the works it cites.
H. Wen, Y. Lin, Y. Xia, H. Wan, Q. Wen, R. Zimmermann, and Y. Liang, “Diffstg: Probabilistic spatio-temporal graph forecasting with denoising diffusion models,” in SIGSPATIAL , 2023, pp. 60:1–60:12
2023
Later among the works it cites.
S. Q. Nate Gruver, Marc Finzi and A. G. Wilson, “Large language models are zero shot time series forecasters,” in NeurIPS , 2023, pp. 1–29
2023
Later among the works it cites.
M. Jin, Q. Wen, Y. Liang, C. Zhang, S. Xue, X. Wang, J. Zhang, Y. Wang, H. Chen, X. Li et al. , “Large models for time series and spatio-temporal data: A survey and outlook,” arXiv , 2023
2023
Later among the works it cites.
Y. Chen, X. Wang, and G. Xu, “Gatgpt: A pre-trained large language model with graph attention network for spatiotemporal imputation,” arXiv , 2023
2023
Later among the works it cites.
C. Sun, Y. Li, H. Li, and S. Hong, “Test: Text prototype aligned embedding to activate llm’s ability for time series,” in ICLR , 2023
2023
Later among the works it cites.
H. Miao, J. Shen, J. Cao, J. Xia, and S. Wang, “Mba-stnet: Bayes-enhanced discriminative multi-task learning for flow prediction,” IEEE Trans. Knowl. Data Eng. , vol. 35, no. 7, pp. 7164–7177, 2023
2023
Later among the works it cites.
H. Miao, Y. Zhao, C. Guo, B. Yang, Z. Kai, F. Huang, J. Xie, and C. S. Jensen, “A unified replay-based continuous learning framework for spatio-temporal prediction on streaming data,” ICDE , 2024
2024
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Z. Zhou, J. Shi, H. Zhang, Q. Chen, X. Wang, H. Chen, and Y. Wang, “Crest: A credible spatiotemporal learning framework for uncertainty-aware traffic forecasting,” in WSDM , 2024, pp. 1–10
2024
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A. Liu and Y. Zhang, “Spatial-temporal dynamic graph convolutional network with interactive learning for traffic forecasting,” IEEE Trans. Intell. Transp. Syst. , 2024
2024
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Z. Liu, H. Miao, Y. Zhao, C. Liu, K. Zheng, and H. Li, “Lighttr: A lightweight framework for federated trajectory recovery,” in ICDE , 2024
2024
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J. Cai, D. Wang, H. Chen, C. Liu, and Z. Xiao, “Modeling dynamic spatiotemporal user preference for location prediction: a mutually enhanced method,” World Wide Web , vol. 27, no. 2, p. 14, 2024
2024
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M. Jin, S. Wang, L. Ma, Z. Chu, J. Y. Zhang, X. Shi, P.-Y. Chen, Y. Liang, Y.-F. Li, S. Pan et al. , “Time-llm: Time series forecasting by reprogramming large language models,” in ICLR , 2024
2024
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