Abstract: Accurate traffic flow prediction can provide scientific decision support for traffic management departments, which is crucial for alleviating urban traffic congestion, improving overall network operation efficiency, and enhancing service levels. Addressing the issue of insufficient exploration of periodic spatio-temporal features in existing traffic flow prediction models, this paper proposes a Multi-Period Spatio-Temporal Gated Network (MPSTG) method for traffic flow prediction. The MPSTG method first designs decoupled parallel multi-period feature extraction branches to model spatio-temporal features under different periods in independent subspaces, considering the multi-period characteristics embedded in traffic flow data. Then, within each individual period branch, a spatio-temporal feature extraction module combining a gating mechanism and graph attention diffusion convolution is introduced to enhance the model’s ability to capture dynamic spatial correlations and temporal dependencies. Finally, a bidirectional feature fusion strategy is constructed to achieve efficient collaborative expression of multi-period information for features of different granularities. Experiments on three public traffic flow datasets show that the proposed method outperforms baseline models. In terms of MAE, it reduces the error by 2.0%, 3.4%, and 3.6% in the 60-minute prediction task on the three datasets, demonstrating its accuracy, adaptability, and robustness in complex traffic scenarios.

摘要： 精准的交通流预测能够为交通管理部门提供科学的决策支持，对缓解城市交通拥堵、提升路网整体运行效率与服务水平具有重要意义。针对现有交通流预测模型对周期时空特征挖掘不足的问题，本文提出了一种基于多周期时空门控网络的交通流预测方法。该方法首先针对交通流数据所蕴含的多周期特性，设计了解耦的并行多周期特征提取分支，以在独立子空间中建模不同周期下的时空特征；随后，在单个周期分支内，引入融合门控机制与图注意力扩散卷积的时空特征提取模块，以增强模型对动态空间相关性与时间依赖关系的刻画能力；最后，针对不同粒度的周期时空特征，构建了一种双向特征融合策略，实现多周期信息的高效协同表达。在三个公开交通流数据集上与主流模型的对比实验结果表明，本文所提方法整体预测性能上均优于基线模型，在平均绝对误差（MAE）指标上，相较于当前最优基线模型，在三个数据集60分钟预测任务中分别降低了 2.0%、3.4% 和 3.6%，验证了其在复杂交通场景下良好的预测精度、适应性和鲁棒性。