A novel generative corrective network structure for traffic forecasting
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00521-024-09906-5.pdf
Reference44 articles.
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4. Ting TJ, Li X, Sanner S, Abdulhai B (2021) Revisiting random forests in a comparative evaluation of graph convolutional neural network variants for traffic prediction. In: 2021 IEEE International intelligent transportation systems conference (ITSC). IEEE, pp 1259–1265
5. Xu H, Jiang C (2020) Deep belief network-based support vector regression method for traffic flow forecasting. Neural Comput Appl 32:2027–2036
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