DST-GTN: Dynamic Spatio-Temporal Graph Transformer Network for Traffic Forecasting
Not on the map. Why?
Abstract (arXiv)
Accurate traffic forecasting is essential for effective urban planning and congestion management. Deep learning (DL) approaches have gained colossal success in traffic forecasting but still face challenges in capturing the intricacies of traffic dynamics. In this paper, we identify and address this challenges by emphasizing that spatial features are inherently dynamic and change over time. A novel in-depth feature representation, called Dynamic Spatio-Temporal (Dyn-ST) features, is introduced, which encapsulates spatial characteristics across varying times. Moreover, a Dynamic Spatio-Temporal Graph Transformer Network (DST-GTN) is proposed by capturing Dyn-ST features and other dynamic adjacency relations between intersections. The DST-GTN can model dynamic ST relationships between nodes accurately and refine the representation of global and local ST characteristics by adopting adaptive weights in low-pass and all-pass filters, enabling the extraction of Dyn-ST features from traffic time-series data. Through numerical experiments on public datasets, the DST-GTN achieves state-of-the-art performance for a range of traffic forecasting tasks and demonstrates enhanced stability.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 22 of its 39 references that are in the map. First posted on arXiv 18 Apr 2024.
Builds on
22 of 39 references.
References · 22
- 2017Attention Is All You Needcited by 59,932
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2017Graph Attention Networkscited by 8,854
- 2017Inductive Representation Learning on Large Graphscited by 6,284
- 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
- 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
- 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,097
- 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 820
- 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecastingcited by 525
- 2019GMAN: A Graph Multi-Attention Network for Traffic Predictioncited by 433
10 more
- 2015Diffusion-Convolutional Neural Networkscited by 268
- 2020Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecastingcited by 212
- 2018Deep Multi-View Spatial-Temporal Network for Taxi Demand Predictioncited by 162
- 2022Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecastingcited by 149
- 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
- 2023PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Predictioncited by 135
- 2021Graph Neural Controlled Differential Equations for Traffic Forecastingcited by 133
- 2022Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecastingcited by 122
- 2021Adaptive Kernel Graph Neural Networkcited by 15
- 2022Vision Transformers: State of the Art and Research Challengescited by 7
Built on by
Citing papers per year: 2025: 1.
1 paper.