Multitask hypergraph convolutional networks: A heterogeneous traffic prediction framework
J Wang, Y Zhang, L Wang, Y Hu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Traffic prediction methods on a single-source data have achieved excellent results in recent
years, especially the Graph Convolutional Networks (GCN) based models with spatio …
years, especially the Graph Convolutional Networks (GCN) based models with spatio …
Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution
Traffic prediction is the cornerstone of intelligent transportation system. Accurate traffic
forecasting is essential for the applications of smart cities, ie, intelligent traffic management …
forecasting is essential for the applications of smart cities, ie, intelligent traffic management …
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 …
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 …
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 …
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 …
Multiple information spatial–temporal attention based graph convolution network for traffic prediction
S Tao, H Zhang, F Yang, Y Wu, C Li - Applied Soft Computing, 2023 - Elsevier
Traffic prediction (forecasting) is a key problem in intelligent transportation. It helps
engineers to obtain traffic trends in advance so that they can make favorable decisions …
engineers to obtain traffic trends in advance so that they can make favorable decisions …
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 …
A graph and attentive multi-path convolutional network for traffic prediction
Traffic prediction is an important and yet highly challenging problem due to the complexity
and constantly changing nature of traffic systems. To address the challenges, we propose a …
and constantly changing nature of traffic systems. To address the challenges, we propose a …
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 …