An interpretable model for short term traffic flow prediction
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DOI: 10.1016/j.matcom.2019.12.013
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Cited by:
- Daniel Vélez-Serrano & Alejandro Álvaro-Meca & Fernando Sebastián-Huerta & Jose Vélez-Serrano, 2021. "Spatio-Temporal Traffic Flow Prediction in Madrid: An Application of Residual Convolutional Neural Networks," Mathematics, MDPI, vol. 9(9), pages 1-16, May.
- Ismail Shah & Izhar Muhammad & Sajid Ali & Saira Ahmed & Mohammed M. A. Almazah & A. Y. Al-Rezami, 2022. "Forecasting Day-Ahead Traffic Flow Using Functional Time Series Approach," Mathematics, MDPI, vol. 10(22), pages 1-16, November.
- Wang, Ke & Ma, Changxi & Qiao, Yihuan & Lu, Xijin & Hao, Weining & Dong, Sheng, 2021. "A hybrid deep learning model with 1DCNN-LSTM-Attention networks for short-term traffic flow prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
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Keywords
Deep architecture; GMDH; Short term traffic flow prediction;All these keywords.
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