Abstract:

Traffic flow prediction is a key core technology in Intelligent Transportation Systems (ITS) and has significant value in improving the efficiency of urban traffic management. Time series prediction and machine learning models are widely used in traditional methods; however, spatio-temporal Graph Neural Networks (GNN) have become a research hotspot owing to their outstanding advantages in traffic flow representation learning. However, existing methods have significant limitations. First, they rely on static graph structures, making it difficult to model long-range spatial dependencies and regional differences. Second, they fail to capture the time delay effect between road segments, resulting in insufficient modeling of spatio-temporal dependencies. To address these issues, this study proposes a Space Dynamic Graph Convolutional (SDGC) traffic flow prediction model based on the Time-Lag Aware Attention (TLAA) mechanism. The encoder—decoder architecture is adopted, and the mechanism is combined with spatial dynamic graph convolution to mitigate the adverse effects of the time-delay phenomenon on prediction accuracy. Specifically, gated causal convolution incorporates attention mechanisms to explicitly model the asynchronous temporal dependencies of spatial nodes by learning delay parameters. The dynamic adaptive spatial correlation matrix can perceive real-time changes in traffic status and accurately depict the dynamic propagation process of traffic flow in the road network. Meanwhile, the introduction of spatio-temporal embedding enables the model to accurately depict the short-term fluctuations, long-term periodic patterns, and spatio-temporal heterogeneity of traffic flow under network topology constraints. Experiments on public benchmark datasets demonstrate that this method effectively addresses challenges related to time lag and dynamic behavior. Compared with existing state-of-the-art baseline models, it achieves significant reductions in key prediction error metrics Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), ranging from 6.5% to 11.9%.

Key words: traffic flow prediction, time-lag aware, attention, gated causal convolution, dynamic adaptive space

摘要：

交通流量预测是智能交通系统(ITS)的关键核心技术, 对提升城市交通管理效能具有重要价值。传统方法中, 时间序列预测和机器学习模型应用广泛, 但时空图神经网络(GNN)因在交通流表示学习中的突出优势已成为研究热点。然而, 现有方法存在显著局限性: 一是依赖静态图结构, 难以建模远距离空间依赖关系和区域差异; 二是未能捕捉路段间时滞效应, 导致时空依赖关系建模不足。针对这些问题, 提出基于时滞感知注意力(TLAA)机制的空间动态图卷积(SDGC)交通流预测模型, 采用编码器-解码器架构, 结合该机制与SDGC, 有效缓解了时滞效应对预测性能的负面影响。门控因果卷积融入注意力机制, 通过学习延迟参数显式建模空间节点的非同步时间依赖; 动态自适应空间相关矩阵实时感知交通状态变化, 精准刻画交通流在路网中的动态传播过程。同时, 时空嵌入的引入使模型能准确刻画交通流的短时波动、长期周期规律及网络拓扑约束下的时空异质性。在公开基准数据集上的实验结果表明, 该方法在应对时滞与动态性挑战上效果显著, 相较于现有最佳基线模型, 其关键预测误差指标平均绝对误差(MAE)和均方根误差(RMSE)实现了6.5%~11.9%的显著降低。

关键词: 交通流量预测, 时滞感知, 注意力, 门控因果卷积, 动态自适应空间