T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction Ling Zhao, Yujiao Song, Chao Zhang, Yu Liu, Pu Wang, Tao Lin, Min Deng and Haifeng Li, Member, IEEE Abstract—Accurate and real-time traffic forecasting plays an important role in the Intelligent Traffic System and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an open scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time, namely, spatial dependence and temporal dependence. To capture the spatial and temporal dependence simultaneously, we propose a novel neural network-based traffic forecasting method, the temporal graph convolutional network (T-GCN) model, which is in combination with the graph convolutional network (GCN) and gated recurrent unit (GRU). Specifically, the GCN is used to learn complex topological structures to capture spatial dependence and the gated recurrent unit is used to learn dynamic changes of traffic data to capture temporal dependence. Then, the T-GCN model is employed to traffic forecasting based on the urban road network. Experiments demonstrate that our T-GCN model can obtain the spatio-temporal correlation from traffic data and the predictions outperform state-of-art baselines on real-world traffic datasets. Our tensorflow implementation of the T-GCN is available at https://github.com/lehaifeng/T-GCN. Index Terms—Traffic forecasting, Temporal Graph Convolutional Network (T-GCN), spatial dependence, temporal dependence. T-GCN is a neural network-based traffic forecasting method that combines graph convolutional networks (GCN) and gated recurrent units (GRU) to capture both spatial and temporal dependencies in traffic data. The GCN is used to model spatial dependencies by learning the topological structure of the urban road network, while the GRU is used to model temporal dependencies by capturing the dynamic changes in traffic data. The T-GCN model is evaluated on two real-world traffic datasets, the SZ-taxi dataset and the Los-loop dataset, and shows superior performance compared to state-of-the-art baselines. The model is robust to noise and can handle both short-term and long-term traffic prediction tasks. The T-GCN model is able to capture spatio-temporal features from traffic data and is not limited to traffic forecasting but can also be applied to other spatio-temporal tasks.T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction Ling Zhao, Yujiao Song, Chao Zhang, Yu Liu, Pu Wang, Tao Lin, Min Deng and Haifeng Li, Member, IEEE Abstract—Accurate and real-time traffic forecasting plays an important role in the Intelligent Traffic System and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an open scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time, namely, spatial dependence and temporal dependence. To capture the spatial and temporal dependence simultaneously, we propose a novel neural network-based traffic forecasting method, the temporal graph convolutional network (T-GCN) model, which is in combination with the graph convolutional network (GCN) and gated recurrent unit (GRU). Specifically, the GCN is used to learn complex topological structures to capture spatial dependence and the gated recurrent unit is used to learn dynamic changes of traffic data to capture temporal dependence. Then, the T-GCN model is employed to traffic forecasting based on the urban road network. Experiments demonstrate that our T-GCN model can obtain the spatio-temporal correlation from traffic data and the predictions outperform state-of-art baselines on real-world traffic datasets. Our tensorflow implementation of the T-GCN is available at https://github.com/lehaifeng/T-GCN. Index Terms—Traffic forecasting, Temporal Graph Convolutional Network (T-GCN), spatial dependence, temporal dependence. T-GCN is a neural network-based traffic forecasting method that combines graph convolutional networks (GCN) and gated recurrent units (GRU) to capture both spatial and temporal dependencies in traffic data. The GCN is used to model spatial dependencies by learning the topological structure of the urban road network, while the GRU is used to model temporal dependencies by capturing the dynamic changes in traffic data. The T-GCN model is evaluated on two real-world traffic datasets, the SZ-taxi dataset and the Los-loop dataset, and shows superior performance compared to state-of-the-art baselines. The model is robust to noise and can handle both short-term and long-term traffic prediction tasks. The T-GCN model is able to capture spatio-temporal features from traffic data and is not limited to traffic forecasting but can also be applied to other spatio-temporal tasks.