Accurate metric depth completion across wide depth ranges is critical for autonomous systems. However, existing methods often struggle to efficiently capture depth features at both close and long ranges, primarily due to the inadequate modeling of fine-grained depth cues specific to different depth ranges. To address these limitations, we propose AirDC, an adaptive iterative depth refinement framework for full-range metric depth completion. The core contributions of our model lie in the design of two key modules. Specifically, we first construct an adaptive fine-grained stereo-LiDAR feature fusion module to fundamentally strengthen the model's capacity to preserve original full-range depth information. Built upon metric-aligned depth volumes (i.e., a 3D representation composed of cubic voxels uniformly partitioned in real-world metric space), this module employs an adaptive sub-voxel depth attention mechanism to enhance sensitivity to subtle depth variations across the full range, thereby both avoiding long-range accuracy degradation introduced by conventional disparity conversion and alleviating the coarse near-range granularity inherent in metric depth representations. Second, we introduce an iterative hypothesis-guided depth refinement module to improve prediction accuracy while maintaining memory efficiency. By integrating multi-scale multi-modal guidance information from depth hypotheses, this module enables explicit and progressive refinement of the initial depth estimation with a small parameter overhead. Experiments on multiple mainstream real-world and synthetic benchmarks demonstrate that AirDC achieves state-of-the-art performance, providing an effective solution for full-range metric depth completion. The code and data are available at https://github.com/yunqidu/AirDC.

Mining traffic congestion dynamics presents difficulties in data structure and spatiotemporal analysis. Existing studies mainly provide insights from a supply perspective, with a restricted examination of why congestion occurs and how travel demands affect congestion. This study introduces an innovative framework to mine congestion dynamics from the perspective of human mobility. In human mobility modeling, we refine the conventional origin-destination representation of activity flow by introducing "path flow" (PF) which considers space-time paths and movement patterns. In congestion scenarios, congestion-related path flow (CPF) and congested path sub-flow (CPSF) are extended to track individuals’ congestion exposure and explore the correlations between congestion and human mobility. To finely classify congestion-related travel demands, a Bayesian inference approach, incorporating destination and spatiotemporal heterogeneities, is developed to deduce trip purposes. The experiments conducted in Wuhan demonstrate the availability and importance of PF in spatiotemporal dynamics analysis of human mobility. Interestingly, we find that 1) the job-housing relationship is imbalanced, with massive residents opting for cross-district living and working; 2) individuals tend to visit tertiary hospitals on weekends and secondary medical facilities with less congestion on weekdays. Notably, path flow can promote the fine-grained modeling of human mobility and provide theoretical support for many urban issues.