Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

14 Sep 2017 Bing Yu Haoteng Yin Zhanxing Zhu

Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect spatial and temporal dependencies... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Traffic Prediction METR-LA STGCN MAE @ 12 step 4.45 # 7
Traffic Prediction PeMS-M STGCN MAE (60 min) 4.02 # 4

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