CurST-Net: Curriculum Learning Guided Spatial–Temporal Network for Traffic Flow Prediction

Traffic flow prediction is crucial to intelligent transportation systems (ITSs). However, the existing methods usually ignore the problem that the prediction difficulty of nodes in the traffic network is actually different. Besides, they fail to effectively handle the dilution of the original semantic information passed layer by layer when capturing the global dependency. To address these issues, this article proposes a curriculum learning guided spatial–temporal network (CurST-Net). Inspired by the human learning process, CurST-Net introduces a curriculum learning (CL) module that defines four metrics from multiple views to evaluate node difficulty and uses a training scheduler (TNS) to gradually introduce easy-to-difficult training nodes to the model to improve prediction ability. Moreover, we design a global spatial–temporal encoder that uses a multihead spatial–temporal attention mechanism and performs interlayer residual scaling on the original semantic information to efficiently capture the global spatial–temporal correlation of nodes. To the best of our knowledge, this is the first work that uses CL to solve the varying prediction difficulty of nodes in traffic flow prediction. The effectiveness of our model is validated through extensive experiments with 12 baseline models on three real-world traffic datasets. For the 1-h prediction horizon, the MAE values of our model on the three datasets are 15.19, 19.22, and 15.50, respectively. Our method yields an overall reduction of 6.51% in MAE across all three datasets compared to DSTAGNN, an advanced traffic flow prediction method. Additionally, the inference time on the PEMSD8 dataset is 5.44 s, requiring only 45% of the inference time of DSTAGNN, which demonstrates a significant improvement in inference speed.