A variant hybrid graph convolution module is designed, which combines Chebyshev polynomial approximation and adaptive adjacency matrix, while balancing computational efficiency and enhancing the model’s ability to capture long-distance spatial dependencies.
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
The fusion of multi-source traffic data and dynamic traffic-state prediction provides an important approach for Intelligent Transportation Systems (ITS) applications. To address spatiotemporal-scale inconsistency among heterogeneous traffic data and insufficient representation of road-network correlations, a multi-sour...
Zheng-Xuan Jiang· 2026 International Conferenc...· 0 citations
The rapid growth in traffic volumes has increased the demand for traffic-flow forecasting models with stronger prediction capability. Traditional methods that rely on local feature extraction and static spatial graph construction can no longer fully meet these requirements. To address the short- and long-term fluctuati...
Hai Yan, Rui Jian, Cheng-Cheng Wang et al.· Algorithms· 0 citations
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...
A closed-loop integration framework that combines a particle swarm optimization (PSO)-based Temporal Fusion Transformer-Graph Attention Network (TFT-GAT) prediction model with Deep Double-Q Network (D3QN) scheduling optimization is proposed, validating the proposed framework’s robustness in spatiotemporal coupling, unc...
A novel method called adaptive diffused spatiotemporal graph convolution network (ADSTGCN) is proposed for accurate traffic flow prediction and achieves superior performance compared to other state-of-the-art methods.
Xiao Luo, Shanshan Wang, Shao-Bao Li et al.· Journal of Transportation En...· 0 citations
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