How to build a graph-based deep learning architecture in traffic domain: A survey
In recent years, various deep learning architectures have been proposed to solve complex
challenges (eg spatial dependency, temporal dependency) in traffic domain, which have …
challenges (eg spatial dependency, temporal dependency) in traffic domain, which have …
AdapGL: An adaptive graph learning algorithm for traffic prediction based on spatiotemporal neural networks
With well-defined graphs, graph convolution based spatiotemporal neural networks for traffic
prediction have achieved great performance in numerous tasks. Compared to other …
prediction have achieved great performance in numerous tasks. Compared to other …
Traffic-GGNN: predicting traffic flow via attentional spatial-temporal gated graph neural networks
Y Wang, J Zheng, Y Du, C Huang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Recent spatial-temporal graph-based deep learning methods for Traffic Flow Prediction
(TFP) problems have shown superior performance in modeling higher-level spatial …
(TFP) problems have shown superior performance in modeling higher-level spatial …
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 …
Learning multiaspect traffic couplings by multirelational graph attention networks for traffic prediction
J Huang, K Luo, L Cao, Y Wen… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Temporal traffic prediction is critical for ITS yet remains challenging in handling complex
spatio-temporal dynamics of traffic systems. The continuous traffic data (eg, traffic flow, and …
spatio-temporal dynamics of traffic systems. The continuous traffic data (eg, traffic flow, and …
Traffic flow prediction via spatial temporal graph neural network
Traffic flow analysis, prediction and management are keystones for building smart cities in
the new era. With the help of deep neural networks and big traffic data, we can better …
the new era. With the help of deep neural networks and big traffic data, we can better …
Multi-stage attention spatial-temporal graph networks for traffic prediction
Accurate traffic prediction plays an important role in Intelligent Transportation System. This
problem is very challenging due to the heterogeneity and dynamic spatio-temporal …
problem is very challenging due to the heterogeneity and dynamic spatio-temporal …
Optimized graph convolution recurrent neural network for traffic prediction
Traffic prediction is a core problem in the intelligent transportation system and has broad
applications in the transportation management and planning, and the main challenge of this …
applications in the transportation management and planning, and the main challenge of this …
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 …
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 …