Conference
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Open access
Aug 2026
A Robust Spatio-Temporal Graph Attention Network for Traffic Flow Forecasting Under Sudden Disturbances
The robust spatiotemporal graph attention network is put forward, which brings together a road network topological encoding, a disturbance factor mapping, a temporal dependency extraction and a strong loss constraint mechanism.
L.-M. Chen, Z.-H. Jiang, J. Yang
· Advanced Electromagnetics · 0 citations
Open access
Aug 2026
An Input-Conditioned Dynamic Hierarchical Spatiotemporal Graph Convolutional Network for Traffic Flow Prediction
Overall, DH-STGCN provides a flexible input-conditioned hierarchical representation for multistep traffic flow prediction, and Controlled hierarchy comparisons favor the window-conditioned assignment over fixed-uniform, static-hard, globally shared, and alternative differentiable assignments.
Jinghao Hu, Yan He, Run-Kui Li et al.
· Applied Sciences · 0 citations
Open access
Aug 2026
Building Urban Traffic Flow Prediction Model Using Spatio-Temporal Graph Convolutional Networks
The proposed position-aware spatio-temporal modeling strategy provides a practical reference for information fusion and dynamic state estimation in large-scale wireless sensing networks and electromagnetic signal-driven monitoring systems, supporting future intelligent perception and communication infrastructures.
J. Sun, Y.-J. Liu, Y.-L. Dou et al.
· Advanced Electromagnetics · 0 citations
#diffusion models
Sep 2026
ASTDGCN: An Adaptive Spatial-Temporal Diffusion Graph Convolutional Network for Traffic Forecasting
An adaptive spatial–temporal diffusion graph convolutional network (ASTD-GCN) is advanced for a traffic flow prediction model that integrates adaptive graph learning, diffusion convolution, and bi-directional long short-term memory network (Bi-LSTM) with attention mechanism, showing better predictive precision in traff...
Su-Min Li, Yi-Na Gao, Hong-Nian Zhu
· SAE technical paper series · 0 citations
Demand prediction model based on spatiotemporal graph neural network and external knowledge fusion
To address the issues of insufficient spatio-temporal dependency modeling and insufficient utilization of external semantic information in the demand forecasting task in complex urban systems, this paper proposes a novel hybrid prediction model STKG-DemandNet that integrates spatio-temporal graph neural networks and ex...
Wen-Di Liao
· International Conference on... · 0 citations
#graph neural networks
Sep 2026
A dynamic graph convolutional network with multiscaled attention for traffic prediction
This paper aims to propose attention-based dynamic graph convolutional recurrent neural network (ADGCRNN) for highway traffic flow prediction, which outperforms state-of-the-art baseline models and realizes multiresolution temporal fusion via self-attention.
Wei-Long Ding, Rui-Zhi Xue, Qi Yu et al.
· International Journal of Web... · 0 citations