GMTP: Enhanced Travel Time Prediction with Graph Attention Network and BERT Integration

(1) Background: Existing Vehicle travel time prediction applications face challenges in modeling complex road network and handling irregular spatiotemporal traffic state propagation. (2) Methods: To address these issues, we propose a Graph Attention-based Multi-Spatiotemporal Features for Travel Time Prediction (GMTP) model, which integrates an enhanced graph attention network (GATv2) and Bidirectional Encoder Representations from Transformers (BERT) to analyze dynamic correlations across spatial and temporal dimensions. The pre-training process consists of two blocks: the Road Segment Interaction Pattern to Enhance GATv2, which generates road segment representation vectors, and a traffic congestion-aware trajectory encoder by incorporating a shared attention mechanism for high computational efficiency. Additionally, two self-supervised tasks are designed for improved model accuracy and robustness. (3) Results: The fine-tuned model had comparatively optimal performance metrics with significant reductions in Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). (4) Conclusions: Ultimately, the integration of this model into travel time prediction, based on two large-scale real-world trajectory datasets, demonstrates enhanced performance and computational efficiency.