DCENet: A dynamic correlation evolve network for short-term traffic prediction
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DOI: 10.1016/j.physa.2023.128525
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References listed on IDEAS
- Wang, Jun & Wang, Wenjun & Liu, Xueli & Yu, Wei & Li, Xiaoming & Sun, Peiliang, 2022. "Traffic prediction based on auto spatiotemporal Multi-graph Adversarial Neural Network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 590(C).
- Wang, Bowen & Wang, Jingsheng, 2022. "ST-MGAT:Spatio-temporal multi-head graph attention network for Traffic prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
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Cited by:
- Yang, Di & Li, Hong & Wang, Peng & Yuan, Lihong, 2024. "Multistep traffic speed prediction: A sequence-to-sequence spatio-temporal attention model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
- Taomei Zhu & Maria Jesus Lopez Boada & Beatriz Lopez Boada, 2024. "Adaptive Graph Attention and Long Short-Term Memory-Based Networks for Traffic Prediction," Mathematics, MDPI, vol. 12(2), pages 1-18, January.
- Ma, Changxi & Zhao, Mingxi, 2023. "Spatio-temporal multi-graph convolutional network based on wavelet analysis for vehicle speed prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
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Keywords
Traffic prediction; Dynamic correlations; Self-attention; Encoder–Decoder;All these keywords.
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