Apr 18, 2024 · The DST-GTN can model dynamic ST relationships between nodes accurately and refine the representation of global and local ST characteristics by ...
The DST-GTN can model dynamic ST relationships between nodes accurately and refine the representation of global and local ST characteristics by adopting ...
Apr 18, 2024 · The paper presents a new deep learning model called DST-GTN that can be used to forecast traffic conditions, such as congestion and travel times ...
May 1, 2024 · (2) The proposed DSTGCN is used to boost the adaptive sensitivity of the prediction model to critical spatio-temporal features so that the PI ...
Missing: DST- | Show results with:DST-
May 14, 2024 · In this work, a Dynamic Spatial-Temporal Network (DSTN) based on Joint Latent Space Representation (JLSR) is proposed for traffic forecasting.
此外，提出了一种动态时空图转换网络（DST-GTN），通过捕捉Dyn-ST特征和交叉口之间的其他动态邻接关系，可以准确地建模节点之间的动态ST关系，并通过在低通和全通滤波器中采用自 ...
Apr 18, 2024 · DST-GTN: Dynamic Spatio-Temporal Graph Transformer Network for Traffic Forecasting · no code implementations • 18 Apr 2024 • Songtao Huang ...
Oct 16, 2024 · Spatiotemporal Graph Neural Networks (ST-GNNs) and Transformers have shown significant promise in traffic forecasting by effectively modeling ...
Graph Convolutional Networks for Traffic Forecasting with Missing ...
www.arxiv-sanity-lite.com › ...
DST-GTN: Dynamic Spatio-Temporal Graph Transformer Network for Traffic Forecasting. Songtao Huang, Hongjin Song, Tianqi Jiang, Akbar Telikani, Jun Shen ...
This paper proposes a novel method called the Dynamic Spatio-Temporal Graph Transformer Network (DST-GTN) to improve traffic forecasting.