Traffic forecasting in large-scale urban networks must operate reliably under imperfect sensing conditions, where measurements may contain noise or missing values. Most existing spatio-temporal graph neural networks focus primarily on modeling spatial–temporal dependencies, while paying limited attention to the propagation of irrelevant or unstable information through dynamic graph structures. In this work, we propose a Dynamic Graph Information Bottleneck (DGIB) framework that enhances prediction stability by introducing task-aware representation compression into dynamic graph learning. Instead of relying solely on architectural complexity, DGIB explicitly regulates the information flow within spatio-temporal embeddings through a variational bottleneck objective. The model adaptively constructs time-evolving adjacency matrices, extracts spatial features via graph convolutions, captures temporal dependencies using recurrent modeling, and constrains the latent representation to retain only predictive content relevant to future traffic states. By jointly optimizing topology adaptation and information-theoretic regularization in an end-to-end manner, the proposed framework mitigates the amplification of noisy or redundant signals in dynamic graphs. Experiments on multiple benchmark traffic datasets demonstrate that DGIB achieves competitive forecasting accuracy while maintaining strong robustness under noisy and incomplete data scenarios.

Accurate semantic interpretation of 3D point clouds is essential for the digital transformation of architecture, engineering, and construction (AEC). Building Information Modeling (BIM) depends on both geometric precision and semantic consistency, yet raw scans are typically noisy, redundant, and computationally expensive to process. This work presents an Information Bottleneck (IB) formulation that regularizes latent features to preserve only task-relevant information, yielding compact and interpretable representations within point-based neural networks. Our method, named IB-PC (Information Bottleneck for Point Clouds), introduces an Information Bottleneck (IB) layer as an auxiliary regularization task alongside supervised prediction, encouraging information compression and improving model robustness. Evaluations are conducted on two representative benchmarks, Semantic3D (outdoor) and TUM RGB-D (indoor) across, five criteria: (i) segmentation accuracy, (ii) calibration, (iii) robustness to noise and occlusion, (iv) computational efficiency, and (v) structural fidelity of architectural elements. The IB-regularized model consistently improves mean Intersection over Union (mIoU), overall accuracy, and macro F1, while reducing Expected Calibration Error (ECE) and Negative Log-Likelihood (NLL). The model remains stable under noise, occlusion, and varying point densities, and yields more consistent segmentation of architectural components such as walls, floors, and columns. These improvements are achieved with roughly 30% fewer FLOPs and reduced memory consumption, demonstrating the method’s efficiency and suitability for large-scale scan-to-BIM and digital twin applications.