T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction

Ling Di Zhao · Yujiao Song · Chao Zhang · Yu Liu · Pu Wang · Tao Lin +2 more

3,527 citations0/0 files storedIEEE Transactions on Intelligent Transportation Systems

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Traffic Prediction and Management Techniques Traffic control and management Advanced Graph Neural Networks

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. To capture the spatial and temporal dependences simultaneously, we propose a novel neural network-based traffic forecasting method, the temporal graph convolutional network (T-GCN) model, which is combined with the graph convolutional network (GCN) and the gated recurrent unit (GRU). Specifically, the GCN is used to learn complex topological structures for capturing spatial dependence and the gated recurrent unit is used to learn dynamic changes of traffic data for capturing 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://www.github.com/lehaifeng/T-GCN.

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References

2017Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting 2014Traffic Flow Prediction With Big Data: A Deep Learning Approach 2017Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction 2003Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results 2016Using LSTM and GRU neural network methods for traffic flow prediction 2004Travel-Time Prediction With Support Vector Regression 2014Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning 1984Dynamic prediction of traffic volume through Kalman filtering theory

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2022Graph neural network for traffic forecasting: A survey 2021Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey 2022Deep Learning on Traffic Prediction: Methods, Analysis, and Future Directions 2020Traffic Flow Prediction via Spatial Temporal Graph Neural Network

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