摘要：

针对当前交通流预测模型对数据间复杂时空相关性挖掘不充分的问题, 提出一种基于解耦动态时空卷积循环网络(DDSTCRN)的交通流预测模型。首先, 在数据解耦模块中, 通过门控机制和残差分解机制解耦出交通数据中所包含的两种不同的隐藏时间序列信号, 即扩散信号和独立信号。其次, 依据两种信号的特点进行单独建模以提高预测精度, 针对扩散信号采用局部扩散卷积捕获交通数据间的扩散过程, 对独立信号采用动态循环图卷积捕获交通数据的全局时空相关性, 解决单一建模导致精度下降的问题。然后, 在图卷积过程中, 通过动态图构造器中无需先验知识的动态图构造方法来捕获交通数据间动态变化的空间依赖关系。最后, 通过外部组件模块预测天气条件等外部因素对交通数据产生的影响以提高模型的鲁棒性。在METR-LA、PEMS-BAY、PEMS04、PEMS08和NE-BJ等5个公开交通流数据集上的实验结果显示, 所提模型相较于表现最优的D²STGNN模型在不同预测长度下的平均绝对误差(MAE)下降了1.2%~4.6%, 与表现次优的DGCRN模型相比, 所提模型在不同预测长度下MAE下降了3.7%~10.5%;与其他代表性模型相比, 所提模型预测误差均有下降。实验结果表明, 所提模型能充分挖掘交通数据中的复杂时空相关性, 并在交通流预测任务上有较好的预测效果。

关键词: 智能交通, 时空特征, 图卷积网络, 门控循环单元, 解耦

Abstract:

This paper introduces a traffic flow prediction model using a Decoupled Dynamic Spatio-Temporal Convolutional Recurrent Network (DDSTCRN) to improve the exploration of complex spatio-temporal correlations in existing traffic flow prediction models. First, the data-decoupling module uses gating and residual decomposition mechanisms to separate the two hidden time-series signals in the traffic data: diffusion and independent signals. Second, separate models are applied to these two signals to improve prediction accuracy. Local diffusion convolution captures the diffusion process between traffic data points, whereas dynamic recurrent graph convolution captures the global spatio-temporal correlations in the independent signals, addressing the accuracy issues of single-model approaches. Third, a dynamic graph constructor, using a prior-free dynamic graph construction method, captures the dynamically changing spatial dependencies in traffic data. Finally, an external component module predicts the impact of factors such as weather conditions on traffic data, thereby enhancing the robustness of the model. Experiments on five public traffic flow datasets (METR-LA, PEMS-BAY, PEMS04, PEMS08, and NE-BJ) show that the proposed model reduces Mean Absolute Error (MAE) by 1.2%—4.6% compared to the top-performing D²STGNN and by 3.7%—10.5% compared to the second-best DGCRN across different prediction lengths. The proposed model exhibits lower prediction errors than the other representative models. The experimental results suggest that the model effectively captures complex spatio-temporal correlations in traffic data and delivers superior performance in traffic flow prediction tasks.

Key words: intelligent transportation, spatio-temporal feature, Graph Convolution Network (GCN), Gated Recurrent Unit (GRU), decoupling