GAMMA-Net融合图注意力与多轴Mamba，提升长时交通预测精度。
GAMMA-Net: Adaptive Long-Horizon Traffic Spatio-Temporal Forecasting Model based on Interleaved Graph Attention and Multi-Axis Mamba
- 用动态图注意力建模交通网络实时空间依赖
- 多轴Mamba高效捕捉长期时空变化，降低计算开销
- 在6个数据集上比基线模型降低16.25%误差，适合智能交通系统
精准交通预测对智能交通系统至关重要，有助于交通管理、缓解拥堵和城市规划。然而，传统模型难以充分捕捉交通数据中的复杂时空依赖关系。为此，本文提出GAMMA-Net，将图注意力网络（GAT）与多轴选择性状态空间模型（Mamba）结合。GAT通过自注意力机制动态调整交通网络中节点的影响，实现基于实时条件的自适应空间建模；同时，Mamba模块高效建模长期时空动态，避免传统循环结构带来的高计算成本。在METR-LA、PEMS-BAY、PEMS03、PEMS04、PEMS07和PEMS08等多个基准数据集上的大量实验表明，GAMMA-Net在不同预测时长下均优于现有先进模型，相较于基线模型最高可降低16.25%的平均绝对误差（MAE）。消融实验验证了时空组件的关键贡献及其互补作用。结论表明，GAMMA-Net树立了交通预测新标准，为下一代交通管理和城市规划提供有力工具。代码已开源：https://github.com/hdy6438/GAMMA-Net。
原文摘要 · Abstract (English)
Accurate traffic forecasting is crucial for intelligent transportation systems, supporting effective traffic management, congestion reduction, and informed urban planning. However, traditional models often fail to adequately capture the intricate spatio-temporal dependencies present in traffic data. To overcome these limitations, we introduce GAMMA-Net, a novel approach that integrates Graph Attention Networks (GAT) with multi-axis Selective State Space Models (Mamba). The GAT component uses a self-attention mechanism to dynamically adjust the influence of nodes within the traffic network, enabling adaptive spatial dependency modeling based on real-time conditions. Simultaneously, the Mamba module efficiently models long-term temporal and spatial dynamics without the heavy computational cost of conventional recurrent architectures. Extensive experiments on several benchmark traffic datasets, including METR-LA, PEMS-BAY, PEMS03, PEMS04, PEMS07, and PEMS08, show that GAMMA-Net consistently outperforms existing state-of-the-art models across different prediction horizons, achieving up to a 16.25% reduction in Mean Absolute Error (MAE) compared to baseline models. Ablation studies highlight the critical contributions of both the spatial and temporal components, emphasizing their complementary role in improving prediction accuracy. In conclusion, the GAMMA-Net model sets a new standard in traffic forecasting, offering a powerful tool for next-generation traffic management and urban planning. The code for this study is available at https://github.com/hdy6438/GAMMA-Net
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