T-GCN: A temporal graph convolutional network for traffic prediction
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 …
system and is of great significance for urban traffic planning, traffic management, and traffic …
A3t-gcn: Attention temporal graph convolutional network for traffic forecasting
J Bai, J Zhu, Y Song, L Zhao, Z Hou, R Du… - … International Journal of …, 2021 - mdpi.com
Accurate real-time traffic forecasting is a core technological problem against the
implementation of the intelligent transportation system. However, it remains challenging …
implementation of the intelligent transportation system. However, it remains challenging …
Hierarchical graph convolution network for traffic forecasting
Traffic forecasting is attracting considerable interest due to its widespread application in
intelligent transportation systems. Given the complex and dynamic traffic data, many …
intelligent transportation systems. Given the complex and dynamic traffic data, many …
Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting
Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due
to the time-varying traffic patterns and the complicated spatial dependencies on road …
to the time-varying traffic patterns and the complicated spatial dependencies on road …
Dynamic traffic correlations based spatio-temporal graph convolutional network for urban traffic prediction
Accurate urban traffic prediction is a critical issue in Intelligent Transportation Systems (ITS).
It is challenging since urban traffic usually indicates high dynamic spatio-temporal …
It is challenging since urban traffic usually indicates high dynamic spatio-temporal …
Dual dynamic spatial-temporal graph convolution network for traffic prediction
Recently, Graph Convolution Network (GCN) and Temporal Convolution Network (TCN) are
introduced into traffic prediction and achieve state-of-the-art performance due to their good …
introduced into traffic prediction and achieve state-of-the-art performance due to their good …
PGCN: Progressive graph convolutional networks for spatial–temporal traffic forecasting
The complex spatial-temporal correlations in transportation networks make the traffic
forecasting problem challenging. Since transportation system inherently possesses graph …
forecasting problem challenging. Since transportation system inherently possesses graph …
Hierarchical spatio–temporal graph convolutional networks and transformer network for traffic flow forecasting
Graph convolutional networks (GCN) have been applied in the traffic flow forecasting tasks
with the graph capability in describing the irregular topology structures of road networks …
with the graph capability in describing the irregular topology structures of road networks …
Spatio-temporal joint graph convolutional networks for traffic forecasting
Recent studies have shifted their focus towards formulating traffic forecasting as a spatio-
temporal graph modeling problem. Typically, they constructed a static spatial graph at each …
temporal graph modeling problem. Typically, they constructed a static spatial graph at each …
DDP-GCN: Multi-graph convolutional network for spatiotemporal traffic forecasting
Traffic speed forecasting is one of the core problems in transportation systems. For a more
accurate prediction, recent studies started using not only the temporal speed patterns but …
accurate prediction, recent studies started using not only the temporal speed patterns but …