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The objective of traffic prediction is to accurately forecast and analyze the dynamics of transportation patterns, considering both space and time.
Principal component analysis
Wold, S., Esbensen, K., and Geladi, P · 1987
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Deep learning: A generic approach for extreme condition traffic forecasting
Yu, R., Li, Y., Shahabi, C., Demiryurek, U., and Liu, Y · 2017
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Deep spatio-temporal residual networks for citywide crowd flows prediction
Zhang, J., Zheng, Y., and Qi, D · 2017
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Diffusion convolutional recurrent neural network: Data-driven traffic forecasting
Li, Y., Yu, R., Shahabi, C., and Liu, Y · 2018
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Deep multi-view spatial-temporal network for taxi demand prediction
Yao, H., Wu, F., Ke, J., Tang, X., Jia, Y., Lu, S., Gong, P., Ye, J., Chuxing, D., and Li, Z · 2018
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Attention based spatial-temporal graph convolutional networks for traffic flow forecasting
Guo, S., Lin, Y., Feng, N., Song, C., and Wan, H · 2019
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Graph wavenet for deep spatial-temporal graph modeling
Wu, Z., Pan, S., Long, G., Jiang, J., and Zhang, C · 2019
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Adaptive graph convolutional recurrent network for traffic forecasting
Bai, L., Yao, L., Li, C., Wang, X., and Wang, C · 2020
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Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., et al · 2020
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Debiased contrastive learning
Chuang, C.-Y., Robinson, J., Lin, Y.-C., Torralba, A., and Jegelka, S · 2020
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Exploiting cloze questions for few shot text classification and natural language inference
Schick, T. and Schütze, H · 2020
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Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting
Song, C., Lin, Y., Guo, S., and Wan, H · 2020
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Understanding contrastive representation learning through alignment and uniformity on the hypersphere
Wang, T. and Isola, P · 2020
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Traffic flow prediction via spatial temporal graph neural network
Wang, X., Ma, Y., Wang, Y., Jin, W., Wang, X., Tang, J., Jia, C., and Yu, J · 2020
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Spatial-temporal transformer networks for traffic flow forecasting
Xu, M., Dai, W., Liu, C., Gao, X., Lin, W., Qi, G.-J., and Xiong, H · 2020
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T-gcn: A temporal graph convolutional network for traffic prediction
Zhao, L., Song, Y., Zhang, C., Liu, Y., Wang, P., Lin, T., Deng, M., and Li, H · 2020
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Traffic flow forecasting with spatial-temporal graph diffusion network
Zhang, X., Huang, C., Xu, Y., Xia, L., Dai, P., Bo, L., Zhang, J., and Zheng, Y · 2021
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Towards spatio- temporal aware traffic time series forecasting
Cirstea, R.-G., Yang, B., Guo, C., Kieu, T., and Pan, S · 2022
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Spatial-temporal hypergraph self-supervised learning for crime prediction
Li, Z., Huang, C., Xia, L., Xu, Y., and Pei, J · 2022
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Msdr: Multi-step dependency relation networks for spatial temporal forecasting
Liu, D., Wang, J., Shang, S., and Han, P · 2022
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Spatio-temporal graph few-shot learning with cross-city knowledge transfer
Lu, B., Gan, X., Zhang, W., Yao, H., Fu, L., and Wang, X · 2022
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Are graph augmentations necessary? simple graph contrastive learning for recommendation
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Gman: A graph multi-attention network for traffic prediction
Zheng, C., Fan, X., Wang, C., and Qi, J · 2020
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Spatial-temporal graph ode networks for traffic flow forecasting
Fang, Z., Long, Q., Song, G., and Xie, K · 2021
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Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting
Han, L., Du, B., Sun, L., Fu, Y., Lv, Y., and Xiong, H · 2021
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Spatial-temporal fusion graph neural networks for traffic flow forecasting
Li, M. and Zhu, Z · 2021
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Understanding the behaviour of contrastive loss
Wang, F. and Liu, H · 2021
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Coupled layer-wise graph convolution for transportation demand prediction
Ye, J., Sun, L., Du, B., Fu, Y., and Xiong, H · 2021
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., and Neubig, G
Cited in the paper.
Yu, J., Yin, H., Xia, X., Chen, T., Cui, L., and Nguyen, Q. V. H · 2022
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Pdformer: Propagation delay-aware dynamic long-range transformer for traffic flow prediction
Jiang, J., Han, C., Zhao, W. X., and Wang, J · 2023
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Gpt-st: Generative pre-training of spatio-temporal graph neural networks
Li, Z., Xia, L., Xu, Y., and Huang, C · 2023
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Repository-level prompt generation for large language models of code
Shrivastava, D., Larochelle, H., and Tarlow, D · 2023
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Visual prompt tuning for generative transfer learning
Sohn, K., Chang, H., Lezama, J., Polania, L., Zhang, H., Hao, Y., Essa, I., and Jiang, L · 2023
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Urbangpt: Spatio-temporal large language models
Li, Z., Xia, L., Tang, J., Xu, Y., Shi, L., Xia, L., Yin, D., and Huang, C · 2024
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