用AI从低精度轨迹还原高精度路径，兼顾隐私与准确性
Restoring Super-High Resolution GPS Mobility Data
- 融合Transformer与图卷积网络，同时捕捉轨迹时序与道路空间关系
- 在真实数据上实现0.198公里的平均弗雷切特距离，优于现有方法
- 适合需要高精度轨迹重建的智慧城市、交通规划等场景
本文提出一种新系统，用于从截断或合成的低分辨率GPS轨迹中重建高分辨率轨迹，解决移动性应用中数据效用与隐私保护之间的关键矛盾。该系统结合基于Transformer的编码器-解码器模型与图卷积网络（GCNs），有效捕捉轨迹数据的时间依赖性及道路网络的空间关系。通过整合这些技术，系统能够恢复因数据截断或四舍五入而丢失的细粒度轨迹信息，这是保护用户隐私的常见做法。我们在北京轨迹数据集上进行评估，结果表明该系统在性能上显著优于传统地图匹配算法和基于LSTM的合成数据生成方法。所提模型的平均弗雷切特距离为0.198公里，远低于地图匹配算法（0.632公里）和合成轨迹模型（0.498公里）。结果表明，该系统不仅能准确重建真实轨迹，还能有效泛化至合成数据。这些发现表明，该系统可应用于城市移动性场景，兼具高精度与强隐私保护能力。
原文摘要 · Abstract (English)
This paper presents a novel system for reconstructing high-resolution GPS trajectory data from truncated or synthetic low-resolution inputs, addressing the critical challenge of balancing data utility with privacy preservation in mobility applications. The system integrates transformer-based encoder-decoder models with graph convolutional networks (GCNs) to effectively capture both the temporal dependencies of trajectory data and the spatial relationships in road networks. By combining these techniques, the system is able to recover fine-grained trajectory details that are lost through data truncation or rounding, a common practice to protect user privacy. We evaluate the system on the Beijing trajectory dataset, demonstrating its superior performance over traditional map-matching algorithms and LSTM-based synthetic data generation methods. The proposed model achieves an average Fréchet distance of 0.198 km, significantly outperforming map-matching algorithms (0.632 km) and synthetic trajectory models (0.498 km). The results show that the system is not only capable of accurately reconstructing real-world trajectories but also generalizes effectively to synthetic data. These findings suggest that the system can be deployed in urban mobility applications, providing both high accuracy and robust privacy protection.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。