GAMMA-Net Introduces Graph Attention and Mamba for Traffic Forecasting
A recent study has unveiled GAMMA-Net, a traffic forecasting model that integrates Graph Attention Networks with multi-axis Selective State Space Models. This innovative approach seeks to address the shortcomings of conventional models in capturing intricate spatio-temporal relationships within traffic data. The Graph Attention Network aspect of GAMMA-Net utilizes self-attention mechanisms to modify node influences in traffic networks adaptively, allowing for real-time spatial dependency modeling. Meanwhile, the Mamba module adeptly captures long-term temporal and spatial dynamics, sidestepping the significant computational demands typical of standard recurrent architectures. The model's efficacy is validated through comprehensive testing on various benchmark traffic datasets, including METR-LA, PEMS-BAY, PEMS03, PEMS04, and PEMS07. This research, identified as arXiv paper 2604.16859v1, underscores the importance of accurate traffic forecasting for enhancing intelligent transportation systems, aiding in traffic management, alleviating congestion, and guiding urban planning.
Key facts
- GAMMA-Net integrates Graph Attention Networks with multi-axis Selective State Space Models
- The model addresses limitations in capturing spatio-temporal dependencies in traffic data
- Graph Attention Networks use self-attention to dynamically adjust node influences
- Mamba modules model long-term temporal and spatial dynamics efficiently
- Experiments were conducted on benchmark datasets including METR-LA and PEMS-BAY
- Accurate traffic forecasting supports intelligent transportation systems
- The research aims to improve traffic management and reduce congestion
- The paper was announced as new on arXiv with identifier 2604.16859v1
Entities
Institutions
- arXiv