Traffic flow prediction based on graph convolutional networks with a parallel attention network and stacked gate recurrent units
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11042-024-19479-z.pdf
Reference61 articles.
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2. Xuan D, Yan Z, Huang S, Liu HX (2018) A similitude theory for modeling traffic flow dynamics. IEEE Trans Intell Transportation Syst PP 1–12. https://doi.org/10.1109/TITS.2018.2837011
3. Xia D, Wang B, Li H, Li Y, Zhang Z (2016) A distributed spatial-temporal weighted model on MapReduce for short-term traffic flow forecasting. Neurocomputing 179:246–263. https://doi.org/10.1016/j.neucom.2015.12.013
4. Zhuang W, Cao Y (2023) Short-term traffic flow prediction based on a k-nearest neighbor and bidirectional long short-term memory model. Appl Sci 13(4):2681. https://doi.org/10.3390/app13042681
5. Belhadi A, Djenouri Y, Djenouri D, Lin JC-W (2020) A recurrent neural network for urban long-term traffic flow forecasting. Appl Intell 50:3252–3265. https://doi.org/10.1007/s10489-020-01716-1
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