《Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting》
摘要：及时准确的交通预测对于城市交通控制与指导至关重要。由于交通流量的非线性和复杂性，传统方法满足不了中长期预测任务的条件且经常忽略时间相关性和空间相关性。在本文中，我们提出了一个深度学习框架Spatio-Temporal Graph Convolutional Networks (STGCN) 以解决交通领域中的时间序列预测问题。我们没有使用普通的卷积单元和循环单元，而是将交通网络定义为图的形式
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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总结
- 3. 论文笔记《Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting》
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相关文章
- 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总结
- 3. 论文笔记《Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting》
- 4. 图卷积神经网络GCN-在交通预测中考虑网络拓扑 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