Accurate prediction of bus arrival time is critical to improving transit service reliability and scheduling efficiency. The estimated time of arrival (ETA) is jointly determined by two processes, namely inter-station travel and station dwell, which are dominated by different traffic mechanisms and thus follow essentially different operating patterns. Most existing methods model the entire route in a unified way, making it difficult to account for both processes simultaneously. This paper therefore proposes a feature-separation prediction method. For inter-station travel time, which is driven by the continuous evolution of traffic flow, we design a Dual-Branch Spatio-Temporal Graph Convolutional Network (DSTGCN); its spectral graph convolution and spatial self-attention branches work together to capture road-network topology and the spatial propagation of traffic flow. For station dwell time, which follows a long-tailed distribution, we design a Spatio-Temporal Graph Convolutional Network with Transformer (STGCN-Trans) and introduce a robust loss function to suppress the effect of extreme outliers. Comparative and ablation experiments show that both models achieve the lowest RMSE, reducing it by 21.7% and 6.7% over the second-best baselines, respectively. They also remain stable under disturbances such as peak hours and extreme weather.

Object detection in real-world scenarios is often challenged by adverse visual conditions, such as low illumination, strong glare, and dense fog, which severely degrade visible-spectrum features and lead to missed detections, inaccurate localization, and reduced detection accuracy. To address these issues, this paper proposes AQFS-Net, a dual-modal fusion detection network for visible-infrared object detection. Built upon YOLOv13, AQFS-Net adopts a symmetric dual-branch backbone by incorporating infrared images, thereby exploiting the complementary information between the visible and infrared modalities. To alleviate the negative transfer caused by conventional static fusion strategies, an Adaptive Quality-Aware Fusion Module (AQFM) is designed to dynamically enhance informative features and suppress degraded information according to the modality-specific reliability of different regions. In addition, a Foreground-Aware Saliency Guidance (FASG) branch is introduced to guide the network to focus on target regions through foreground supervision, reducing interference from complex backgrounds. Experimental results on the public LLVIP and M3FD datasets show that the proposed method improves mAP@0.5 by 6.8 and 3.1 percentage points, respectively, compared with the baseline using only visible images. These results demonstrate the effectiveness of AQFS-Net in improving dual-modal fusion quality and detection performance under challenging visual conditions, providing a practical reference for visible-infrared object detection in complex illumination scenarios.