Toward Time-Continuous Data Inference in Sparse Urban CrowdSensing

Abstract

Sparse Urban CrowdSensing (Sparse UCS) is a practical paradigm for completing full sensing maps from limited observations. However, existing methods typically rely on a time-discrete assumption, where data is considered static within fixed intervals. This simplification introduces significant errors as real-world data changes continuously. To address this, we propose a framework for time-continuous data completion. Our approach, Time-Aware Mamba-based Deep Matrix Factorization (TIME-DMF), leverages the Mamba architecture as a powerful temporal encoder. Crucially, we enhance Mamba with a novel time-aware mechanism that explicitly incorporates the actual, often irregular, physical time intervals between observations into its state transitions. This allows our model to accurately capture true temporal dynamics and generate high-fidelity data for any queried moment in time through a query-generate mechanism. Extensive experiments on five diverse sensing tasks demonstrate that TIME-DMF significantly outperforms state-of-the-art methods, validating the superiority of the time-continuous paradigm for Sparse UCS.