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.

Building individualization is a critical preprocessing step for refined applications of oblique photogrammetry 3D models, yet existing semantic segmentation methods encounter accuracy bottlenecks when applied to ultra-high-resolution orthophotos. To overcome this challenge, this study constructs an automated technical framework following a workflow from orthophoto generation to high-precision semantic segmentation, and finally to dynamic 3D rendering. The framework comprises three stages: (1) converting the 3D model into a 2D orthophoto to ensure that the extracted building contours can be precisely registered with the original 3D model in space; (2) utilizing the proposed Gated-ASPP High-Resolution Network (GA-HRNet) to extract building contours, enhancing segmentation accuracy by synergizing HRNet’s spatial detail preservation capability with ASPP’s multi-scale context awareness; (3) mapping the extracted 2D vector contours back to the 3D model and achieving interactive building individualization via dynamic rendering technology. Evaluated on a custom-built Hong Kong urban building dataset, GA-HRNet achieved an Intersection over Union (IoU) of 91.25%, an F1-Score of 95.41%, a Precision of 93.31%, and a Recall of 97.70%. Its performance surpassed that of various comparative models, including FCN, U-Net, MBR-HRNet, and others, with an IoU lead of 1.46 to 5.62 percentage points. This method enables precise building extraction and dynamic highlighting within 3D scenes, providing an efficient and reliable technical path for the refined application of large-scale urban oblique photogrammetry models.