Seq2-ResGCRN: Sequence-to-Sequence Residual Graph Convolutional Recurrent Network for Traffic Flow Prediction

Accurate traffic flow prediction remains challenging due to the complex spatio-temporal dependencies inherent in road networks. Existing graph-based prediction models typically construct the graph structure from geographical adjacency. This limits spatial feature extraction to physically neighboring nodes. Consequently, potential correlations among non-adjacent nodes within the same region are overlooked. Moreover, these models generally treat the influence of neighboring nodes uniformly, whereas, in practice, such influences are inherently heterogeneous. On the temporal side, many approaches rely on recurrent units such as Long Short-Term Memory (LSTM) or Gated Recurrent Units (GRUs) to capture short-term dependencies, yet these architectures are prone to gradient vanishing or explosion, leading to training instability. To address these issues, this paper proposes Seq2-ResGCRN, an encoder–decoder framework based on residual graph convolutional recurrent networks with an attention mechanism. The model reconstructs the adjacency matrix via graph diffusion convolution to learn adaptive edge weights, thereby capturing heterogeneous spatial influences. Seq2-ResGCRN further integrates graph convolution into the GRU architecture and introduces residual connections to alleviate gradient degradation during training. This study conducts experiments on two open-source real-world datasets (i.e., PEMS04 and PEMS08) to evaluate the Seq2-ResGCRN. The raw data, originally collected at a sampling frequency of 30 s, are aggregated into 5 min time intervals. Each record comprises three features: traffic flow, traffic speed, and road occupancy. The experimental results demonstrate that our Seq2-ResGCRN outperforms state-of-the-art methods, achieving 1.6–2.3% and 2.1–5.5% relative improvements in MAE and RMSE, respectively. Seq2-ResGCRN effectively captures the spatio-temporal correlations of short-term traffic flow and achieves superior predictive performance.