A Dual-Attentional Gated Residual Framework for Robust Travel Time Prediction

Travel time prediction (TTP) is a fundamental pillar of intelligent transportation systems (ITS). However, deploying highly parameterized deep learning models in data-scarce environments—referred to as the “cold-start” problem—remains a critical bottleneck, frequently leading to overfitting and severe error accumulation on ultra-long trajectories. To surmount these limitations, this study proposes the Dual-Attentional Gated Residual Network (DAGRN), a data-efficient forecasting framework driven by a novel topology-temporal coordination mechanism. Specifically, the framework introduces three integrated innovations: (1) transforming the primal network into a physics-aware Line Graph to explicitly filter out illegal movements and dynamically modulating topological propagation via Feature-wise Linear Modulation (FiLM); (2) coupling a Bidirectional GRU backbone with a Multi-Head Attention module to simultaneously capture global trends and localized intersection delays; (3) employing a Gated Residual Fusion mechanism that preserves dimensional consistency and facilitates gradient flow in extensive sequences. To rigorously validate the model’s robustness, we conduct evaluations on a highly constrained, stratified dataset comprising merely 2000 trajectories. Experimental results demonstrate that DAGRN achieves state-of-the-art predictive precision with an RMSE of 415.485 s and an R2 of 0.848, significantly outperforming 12 advanced baseline models and reducing error by up to 13.8% against the strongest graph baseline. Comprehensive ablation studies confirm the absolute necessity of the Multi-Head Attention module, whose removal causes the most severe performance degradation (RMSE surging to 521.495 s). Ultimately, DAGRN presents a readily deployable solution for sparse-data ITS regimes, actively paving the way for future hybrid integrations with microscopic traffic simulations and evolutionary road network optimization algorithms.