《Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting》
摘要：及時準確的交通預測對於城市交通控制與指導至關重要。由於交通流量的非線性和複雜性，傳統方法滿足不了中長期預測任務的條件且經常忽略時間相關性和空間相關性。在本文中，我們提出了一個深度學習框架Spatio-Temporal Graph Convolutional Networks (STGCN) 以解決交通領域中的時間序列預測問題。我們沒有使用普通的卷積單元和循環單元，而是將交通網絡定義爲圖的形式
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- 1. STGCN:Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastin
- 2. Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting總結
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- 5. Paper Notes: A Comprehensive Survey on Graph Neural Networks
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相關文章
- 1. STGCN:Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastin
- 2. Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting總結
- 3. 論文筆記《Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting》
- 4. 圖卷積神經網絡GCN-在交通預測中考慮網絡拓撲 STGCN-A Deep Learning Framework for Traffic
- 5. Paper Notes: A Comprehensive Survey on Graph Neural Networks
- 6. LC-RNN: A Deep Learning Model for Traffic Speed Prediction
- 7. Learning Convolutional Neural Networks for Graphs
- 8. Knowledge Graph Convolutional Networks for Recommender Systems
- 9. PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs
- 10. Few-Shot Learning with Graph Neural Networks