Transcription of Spatio-Temporal Graph Convolutional Networks: A Deep ...
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Spatio-Temporal Graph Convolutional Networks: A Deep ...
Spatio-Temporal Graph Convolutional Networks: a deep learning Frameworkfor Traffic ForecastingBing Yu 1, Haoteng Yin 2,3, Zhanxing Zhu 3,41 School of Mathematical Sciences, Peking University, Beijing, China2 Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China3 Center for Data Science, Peking University, Beijing, China4 Beijing Institute of Big Data Research (BIBDR), Beijing, China{byu, htyin, accurate traffic forecast is crucial for ur-ban traffic control and guidance. Due to the highnonlinearity and complexity of traffic flow, tradi-tional methods cannot satisfy the requirements ofmid-and-long term prediction tasks and often ne-glect spatial and temporal dependencies. In this pa-per, we propose a novel deep learning framework, Spatio-Temporal Graph Convolutional Networks(STGCN), to tackle the time series prediction prob-lem in traffic domain. Instead of applying regu-lar Convolutional and recurrent units, we formulatethe problem on graphs and build the model withcomplete Convolutional structures, which enablemuch faster training speed with fewer show that our model STGCN effec-tively captures comprehensive Spatio-Temporal cor-relations through modeling multi-scale traffic net-works and consistently outperforms state-of-the-artbaselines on various real-world traffic IntroductionTransportation plays a vital role in everybody s daily life.}
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for TrafÞc Forecasting Bing Yu! 1, Haoteng Yin! 2,3, Zhanxing Zhu 3,4 1 School of Mathematical Sciences, Peking University, Beijing, China 2 Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China 3 Center for Data Science, Peking University, Beijing, China
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