Generative Adversarial Nets Model Based on Spatio-Temporal Graph Attention Network for Multi-Step Prediction
- Resource Type
- Conference
- Authors
- Wang, Ling; Wang, Rongxian; Jia, Gaofeng
- Source
- 2023 China Automation Congress (CAC) Automation Congress (CAC), 2023 China. :6954-6959 Nov, 2023
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Automation
Computational modeling
Predictive models
Generative adversarial networks
Data models
Generators
Data mining
Generative Adversarial Nets
Spatio-Temporal Graph Attention Network
Multi-step prediction
Traffic data
- Language
- ISSN
- 2688-0938
The precise forecasting of traffic data holds significant importance for numerous practical applications in the real world. The challenge lies in the nonlinear spatio-temporal dependencies of the data, as well as rapid error accumulations for multi-step predictions. Therefore, we propose a Generative Adversarial Nets model based on a Spatio-Temporal Graph Attention Network (STGAT-GAN). Utilizing spatio-temporal graph attention proves to be an effective method for capturing the spatio-temporal dependencies, while GANs can learn a rich distribution over spatio-temporal data implicitly to produce realistic data adversarially. Experiments are carried out on two public traffic datasets. Comparative experimental analysis reveals that the proposed STGAT-GAN has better performance.