[2603.19474] TRACE: Trajectory Recovery with State Propagation Diffusion for Urban Mobility

Computer Science > Machine Learning arXiv:2603.19474 (cs) [Submitted on 19 Mar 2026] Title:TRACE: Trajectory Recovery with State Propagation Diffusion for Urban Mobility Authors:Jinming Wang, Hai Wang, Hongkai Wen, Geyong Min, Man Luo View a PDF of the paper titled TRACE: Trajectory Recovery with State Propagation Diffusion for Urban Mobility, by Jinming Wang and 4 other authors View PDF HTML (experimental) Abstract:High-quality GPS trajectories are essential for location-based web services and smart city applications, including navigation, ride-sharing and delivery. However, due to low sampling rates and limited infrastructure coverage during data collection, real-world trajectories are often sparse and feature unevenly distributed location points. Recovering these trajectories into dense and continuous forms is essential but challenging, given their complex and irregular spatio-temporal patterns. In this paper, we introduce a novel diffusion model for trajectory recovery named TRACE, which reconstruct dense and continuous trajectories from sparse and incomplete inputs. At the core of TRACE, we propose a State Propagation Diffusion Model (SPDM), which integrates a novel memory mechanism, so that during the denoising process, TRACE can retain and leverage intermediate results from previous steps to effectively reconstruct those hard-to-recover trajectory segments. Extensive experiments on multiple real-world datasets show that TRACE outperforms the state-of-the-art, offeri...