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机译:交通网络的多步速度预测:一种考虑时空依赖性的深度学习方法
Tsinghua Univ, Dept Civil Engn, Beijing 100084, Peoples R China;
Tsinghua Univ, Dept Civil Engn, Beijing 100084, Peoples R China|Tsinghua Univ, Tsinghua Daimler Joint Res Ctr Sustainable Transp, Beijing 100084, Peoples R China;
Tsinghua Univ, Dept Civil Engn, Beijing 100084, Peoples R China|Tsinghua Univ, Tsinghua Daimler Joint Res Ctr Sustainable Transp, Beijing 100084, Peoples R China;
Tsinghua Univ, Dept Civil Engn, Beijing 100084, Peoples R China|Univ Washington, Dept Civil & Environm Engn, Seattle, WA 98195 USA;
Tsinghua Univ, Tsinghua Daimler Joint Res Ctr Sustainable Transp, Beijing 100084, Peoples R China|Tsinghua Univ, Dept Ind Engn, Beijing 100084, Peoples R China;
Traffic forecasting; Deep learning; Attention mechanism; Graph convolution; Multistep prediction; Sequence-to-sequence model;
机译:多步速度预测交通网络:考虑时空依赖性的深度学习方法
机译:城市交通网络的行车速度预测:一种基于路径的深度学习方法
机译:基于多尺度时空特征学习网络的长期交通速度预测
机译:时空广泛学习网络用于交通速度预测 * sup>
机译:具有时空特征的实时短期交通速度预测的深度学习方法
机译:以图像形式学习交通:用于大规模交通网络速度预测的深度卷积神经网络
机译:学习交通图像:一种用于大规模交通网络速度预测的深度卷积神经网络