Dataset description.

To more effectively capture the periodic and dynamic changes in urban traffic flow and the spatiotemporal correlation of complex road networks, a new traffic flow prediction method, the Enhanced Spatiotemporal Graph Convolutional Network Encoder-Decoder Model (ESGCN-EDM), is proposed. The model achieves a significant enhancement in prediction accuracy through the introduction of the attention-based Personalized-enhanced Fusion Graph Convolutional Network (aPFGCN) and the Temporal Convolutional Bidirectional Long Short-Term Memory (TCBiL) module. The aPFGCN module effectively reduces the dimensionality of features and decreases model complexity to obtain the final node feature representation by personalizing the adjustment of node influence coefficients and applying Fourier transform and inverse transform techniques. Additionally, by incorporating an attention mechanism, it enhances the model’s ability to focus on important information and effectively captures the spatial topological relationships within the traffic network. The TCBiL module integrates 1D convolution with BiLSTM to form a unified temporal feature extraction module. The 1D convolution is utilized to extract local features from the time series, while the BiLSTM captures long-term dependencies within the time series. This allows for simultaneous feature extraction and temporal modeling, thereby enhancing the model’s efficiency and performance, and strengthening its ability to model time series. In the encoder part of the ESGCN-EDM, the aPFGCN is combined with the TCBiL to handle the spatiotemporal coupling interactions of the road network. The decoder part then performs multi-step predictions based on spatiotemporal sequences using the TCBiL and CNN, generating high-dimensional representations. Extensive experiments conducted on two real-world road traffic datasets demonstrate that the ESGCN-EDM model consistently outperforms other benchmark models in 1-hour, 30-minute, and 15-minute traffic flow predictions. Specifically, on the PeMSD8 dataset, the model achieves reductions in MAE, RMSE, and SMAPE by 7.9%, 2.1%, and 16.9%, respectively, compared to the AMRGCN model for 1-hour predictions. Similarly, on the PeMSD4 dataset, the model reduces MAE, RMSE, and MAPE by 1.8%, 1.1%, and 3.0%, respectively. These results validate the efficacy of the proposed model and its ability to significantly enhance the accuracy of traffic flow forecasting.为更有效地捕捉城市交通流的周期性与动态变化，以及复杂道路网络的时空相关性，本文提出一种新型交通流预测方法——增强型时空图卷积编码器-解码器模型（Enhanced Spatiotemporal Graph Convolutional Network Encoder-Decoder Model，简称ESGCN-EDM）。该模型通过引入基于注意力机制的个性化融合图卷积网络（attention-based Personalized-enhanced Fusion Graph Convolutional Network，简称aPFGCN）与时间卷积双向长短期记忆（Temporal Convolutional Bidirectional Long Short-Term Memory，简称TCBiL）模块，显著提升了预测精度。aPFGCN模块通过个性化调整节点影响系数，并结合傅里叶变换与逆变换技术，有效降低特征维度与模型复杂度，最终得到节点特征表征；同时借助注意力机制，增强模型对关键信息的聚焦能力，有效捕捉交通网络内的空间拓扑关系。TCBiL模块将一维卷积与双向长短期记忆网络（Bidirectional Long Short-Term Memory, BiLSTM）结合为统一的时间特征提取单元：一维卷积用于提取时间序列的局部特征，BiLSTM则捕捉时间序列内的长期依赖关系，实现特征提取与时序建模同步完成，进而提升模型效率与性能，强化其时序建模能力。在ESGCN-EDM的编码器部分，aPFGCN与TCBiL模块结合以处理道路网络的时空耦合交互；解码器则借助TCBiL与卷积神经网络（Convolutional Neural Network, CNN）基于时空序列完成多步预测，生成高维表征。在两份真实道路交通数据集上开展的大量实验表明，ESGCN-EDM模型在1小时、30分钟与15分钟的交通流预测任务中，性能始终优于其他基准模型。具体而言，在PeMSD8数据集上，针对1小时预测任务，该模型较AMRGCN模型的平均绝对误差（Mean Absolute Error, MAE）、均方根误差（Root Mean Square Error, RMSE）与对称平均绝对百分比误差（Symmetric Mean Absolute Percentage Error, SMAPE）分别降低7.9%、2.1%与16.9%；同样在PeMSD4数据集上，该模型的平均绝对误差、均方根误差与平均绝对百分比误差（Mean Absolute Percentage Error, MAPE）分别降低1.8%、1.1%与3.0%。上述结果验证了所提模型的有效性，证明其可显著提升交通流预测的精度。