- Adaptive Graph Convolutional Recurrent Network for Traffic ...🔍
- Spatio‐temporal adaptive graph convolutional networks for traffic ...🔍
- Time|Evolving Graph Convolutional Recurrent Network for Traffic ...🔍
- Adaptive Graph Convolutional Recurrent Network with Transformer ...🔍
- Adaptive Graph Convolutional Neural Networks🔍
- Graph convolutional dynamic recurrent network with attention for ...🔍
- Attention|based Spatial|Temporal Graph Convolutional ...🔍
- A Decomposition Dynamic graph convolutional recurrent network for ...🔍
Adaptive Graph Convolutional Recurrent Network for Traffic ...
Adaptive Graph Convolutional Recurrent Network for Traffic ...
Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting Download PDF · Open Website · Lei Bai, Lina Yao, Can Li, Xianzhi Wang, Can ...
Adaptive Graph Convolutional Recurrent Network for Traffic ...
A multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster ...
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 ...
Time-Evolving Graph Convolutional Recurrent Network for Traffic ...
Specifically, we first propose a tensor-composing method to generate adaptive time-evolving adjacency graphs. Based on these time-evolving ...
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 ...
Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle graph data such as molecular data, point could ...
TARGCN: temporal attention recurrent graph convolutional neural ...
In the Emb-GCN layer, node embedding matrix and node parameter learning techniques are employed to extract spatial correlations between traffic ...
ST-DAGCN: A Spatiotemporal Dual Adaptive Graph Convolutional ...
Emergence of artificial intelligence has led to the surge of deep learning methods for short-term traffic forecast. Notably, Graph Convolutional Neural Networks ...
Adaptive Graph Convolutional Recurrent Network with Transformer ...
This paper introduces a novel transformer-based adaptive graph convolutional recurrent network. The proposed network automatically infers the interdependencies ...
Graph convolutional dynamic recurrent network with attention for ...
Graph convolutional dynamic recurrent network with attention for traffic forecasting ; Journal: Applied Intelligence, 2023, № 19, p. 22002-22016 ; Publisher: ...
Attention-based Spatial-Temporal Graph Convolutional ... - NASA ADS
In particular, GCRN integrates gated recurrent units and adaptive graph convolutional networks for dynamically learning graph structures and capturing spatial ...
TEA-GCN: Transformer-Enhanced Adaptive Graph Convolutional ...
Ref. [24] proposes a novel spatio-temporal graph neural network model that conjointly captures high-order spatio-temporal relationships and ...
A Decomposition Dynamic graph convolutional recurrent network for ...
Our daily lives are greatly impacted by traffic conditions, making it essential to have accurate predictions of traffic flow within a road network.
Attention-Based Multiple Graph Convolutional Recurrent Network for ...
Experimental results on two real-world traffic datasets demonstrate the superiority of the proposed AMGCRN over state-of-the-art baselines. The results suggest ...
Traffic flow prediction based on adaptive graph convolutional ...
Article "Traffic flow prediction based on adaptive graph convolutional recurrent network" Detailed information of the J-GLOBAL is an information service ...
wavelet-inspired multiscale graph convolutional recurrent network ...
WAVELET-INSPIRED MULTISCALE GRAPH CONVOLUTIONAL RECURRENT NETWORK FOR TRAFFIC FORECASTING · 1: F2GNN: AN ADAPTIVE FILTER WITH FEATURE SEGMENTATION FOR GRAPH- ...
Graph convolutional recurrent network for traffic forecasting with ...
Some methods design different self-adaptive mechanisms to uncover latent graph structures from training data without any prior knowledge (Bai et ...
A decomposition dynamic graph convolutional recurrent network for ...
These sensors typically produce normal signals representing normal traffic flows and abnormal signals indicating unknown traffic disruptions. Graph convolution ...
Adaptive Spatial-Temporal Graph Convolution Networks for ...
Several existing traffic prediction methods have been applied to local spatial-temporal information. Li et.al [16] designed a unified neural ...