Paper Info
Title
T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction
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 <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.github.com/lehaifeng/T-GCN</uri> .
Year
DOI
Venue
2020
10.1109/TITS.2019.2935152
IEEE Transactions on Intelligent Transportation Systems
Keywords
DocType
Volume
Traffic forecasting,temporal graph convolutional network (T-GCN),spatial dependence,temporal dependence
Journal
21
Issue
ISSN
Citations 
9
1524-9050
53
PageRank 
References 
Authors
1.59
0
8
Name
Order
Citations
PageRank
Ling Zhao1542.95
Yujiao Song2531.59
Chao Zhang3939103.66
Yu Liu439334.91
Pu Wang528917.72
Tao Lin6533.62
Min Deng7531.59
Haifeng Li820717.01