- Adaptive Graph Fusion Convolutional Recurrent Network for Traffic ...🔍
- Adaptive Graph Convolutional Recurrent Network for Traffic ...🔍
- [PDF] Adaptive Graph Convolutional Recurrent Network for Traffic ...🔍
- Adaptive Graph Convolutional Recurrent Network with Transformer ...🔍
- Graph convolutional recurrent network for traffic forecasting with ...🔍
- Gated Fusion Adaptive Graph Neural Network for Urban Road ...🔍
- Wavelet|Inspired Multiscale Graph Convolutional Recurrent Network ...🔍
- Dynamic Graph Convolutional Recurrent Network for Traffic Prediction🔍
Adaptive Graph Fusion Convolutional Recurrent Network for Traffic ...
Adaptive Graph Fusion Convolutional Recurrent Network for Traffic ...
An adaptive graph fusion convolution is proposed to discover the changing relationships between traffic volumes without a priori knowledge. It ...
Adaptive Graph Convolutional Recurrent Network for Traffic ...
Recent works focus on designing complicated graph neural network architectures to capture shared patterns with the help of pre-defined graphs. In this paper, we ...
Adaptive Graph Fusion Convolutional Recurrent Network for Traffic ...
Index Terms—Adaptive graph fusion convolution, traffic flow prediction, spatiotemporal dependence. I. INTRODUCTION. WITH the development of society and economy, ...
Adaptive Graph Fusion Convolutional Recurrent Network for Traffic ...
An adaptive graph fusion convolutional recurrent network (AGFCRN) is proposed to model the temporal and spatial characteristics of traffic flow data ...
Adaptive Graph Fusion Convolutional Recurrent Network for Traffic ...
An adaptive graph fusion convolution is proposed to discover the changing relationships between traffic volumes without a priori knowledge. It uses a self- ...
[PDF] Adaptive Graph Convolutional Recurrent Network for Traffic ...
It is argued that learning node-specific patterns is essential for traffic forecasting while the pre-defined graph is avoidable, and two adaptive modules ...
Adaptive Graph Convolutional Recurrent Network for Traffic ...
Furthermore, we combine. NAPL and DAGG with recurrent networks and propose a unified traffic forecasting model - Adaptive. Graph Convolutional Recurrent Network ...
Adaptive Graph Convolutional Recurrent Network with Transformer ...
Consequently, methods based on deep learning for predicting traffic flow have become more popular recently. Notably, Recurrent Neural Networks (RNNs), ...
Adaptive Graph Convolutional Recurrent Network for Traffic ...
Similar Papers · Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting · Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion ...
Graph convolutional recurrent network for traffic forecasting with ...
Specifically, SREM and AMUM are proposed to capture nodes' mutual relations at each time step and to model the evolution of the dynamic ...
Adaptive Graph Convolutional Recurrent Network with Transformer ...
The proposed network automatically infers the interdependencies among different traffic sequences and incorporates the capability to capture ...
Gated Fusion Adaptive Graph Neural Network for Urban Road ...
To overcome this limitation, we propose a network structure that integrates adaptive graph convolution with adaptive graph attention. By ...
AdapGL: An adaptive graph learning algorithm for traffic prediction ...
Experiments on three GCN-based neural networks and four real-world datasets are carried out for the multi-step traffic flow prediction. The results demonstrate ...
Adaptive Graph Convolutional Recurrent Network with Transformer ...
This design approach significantly enhances the model's performance and improves the accuracy of traffic flow prediction. The experimental results on four real ...
Wavelet-Inspired Multiscale Graph Convolutional Recurrent Network ...
The encoded features of all streams are fused by LIDWT and then input to a GCRN-based decoder to restore and predict the traffic metrics signals ...
Dynamic Graph Convolutional Recurrent Network for Traffic Prediction
As an essential part of graph convolution, the dynamic adjacency matrix generation module can learn a dynamic representation of road network implicitly, which ...
Spatio‐temporal adaptive graph convolutional networks for traffic ...
In summary, we propose a novel spatio-temporal adaptive graph convolutional networks model (STAGCN) for traffic flow forecasting. The model ...
Dynamic Graph Convolutional Network with Attention Fusion ... - arXiv
... Adaptive graph convolutional recurrent network for traffic forecasting', in Ad- vances in Neural Information Processing Systems, (2020). [2] ...
jwwthu/GNN4Traffic: This is the repository for the collection ... - GitHub
Adaptive Graph Fusion Convolutional Recurrent Network for Traffic Forecasting[J]. IEEE Internet of Things Journal, 2023. Link. Liao Z, Huang H, Zhao Y, et al ...
A Decomposition Dynamic graph convolutional recurrent network for ...
Graph convolution networks are widely used for traffic prediction due to their ability to capture correlations between network nodes. However, existing ...