TRACE: Trajectory Recovery with State Propagation Diffusion for Urban Mobility

TL;DR

TRACE introduces a diffusion-based model with a novel memory mechanism to accurately recover dense urban trajectories from sparse GPS data, significantly improving reconstruction accuracy for location-based services.

Contribution

The paper presents a new diffusion model with state propagation and memory integration for trajectory recovery, outperforming existing methods on real-world datasets.

Findings

Achieves over 26% accuracy improvement in trajectory reconstruction.

Effectively reconstructs complex and irregular urban mobility patterns.

Maintains low inference overhead despite enhanced accuracy.

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, offering $>$26\% accuracy improvement without significant inference overhead. Our work strengthens the foundation for mobile and web-connected location services, advancing the quality and fairness of data-driven urban applications. Code is available at: https://github.com/JinmingWang/TRACE