STLTEformer: Spatio-Temporal Long-Term Embedding Transformer for Traffic Flow Prediction

Traffic flow prediction is crucial for efficient transportation systems, allowing better traffic management and decision-making. However, it’s a complex challenge due to its intricate spatial and temporal dependencies and fine-grained variations. Various network architectures, particularly graph neural networks (GNN) and transformers, have been proposed to handle these complexities. Nevertheless, as network architectures advanced, their performance improvements started to slow down due to increased complexity. This shift in focus led to an emphasis on input embeddings, which offer a promising way to build simpler, more effective models. This work introduces the spatial–temporal long-term embedding transformer (STLTEformer) framework for precise traffic prediction. A 5, 15, and 30-min interval embedding is designed to capture short-term fluctuations, intermediate trends, and long-term patterns, respectively. These embeddings equip the STLTEformer with the ability to comprehend intricate traffic patterns. Extensive experiments were conducted on four real-world datasets. The results demonstrate that STLTEformer achieves an improvement of up to 13.6% in MAE, 9.4% in RMSE, and 14% in MAPE over popular models based on GNN, and up to a 5% improvement in MAE, 1.9% in RMSE, and 7.5% in MAPE over popular transformer-based methods with good computational efficiency.