用扩散模型恢复稀疏轨迹，提升城市出行数据精度
TRACE: Trajectory Recovery with State Propagation Diffusion for Urban Mobility
- 提出状态传播扩散模型，利用中间结果增强难恢复段落重建
- 在多个真实数据集上提升超26%精度，推理开销小
- 适合导航、网约车等需要高精度轨迹的应用场景
高质量的GPS轨迹对基于位置的网络服务和智慧城市建设至关重要，涵盖导航、拼车和配送等领域。然而，由于采样率低及基础设施覆盖不足，实际轨迹常呈稀疏且点分布不均。将这些轨迹恢复为稠密连续形态至关重要但极具挑战性，因其时空模式复杂且不规则。本文提出一种新型轨迹恢复扩散模型TRACE，可从稀疏不完整输入中重建稠密连续轨迹。核心在于提出的状态传播扩散模型（SPDM），引入新颖记忆机制，在去噪过程中保留并利用前步中间结果，有效重建难以恢复的轨迹段。在多个真实数据集上的大量实验表明，TRACE优于当前最先进方法，精度提升超过26%，且推理开销无显著增加。本工作强化了移动与联网位置服务的数据基础，推动了数据驱动型城市应用的质量与公平性。代码已公开：https://github.com/JinmingWang/TRACE
原文摘要 · Abstract (English)
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
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