仅用起点终点就能补全轨迹，效果优于现有方法。
ProDiff: Prototype-Guided Diffusion for Minimal Information Trajectory Imputation
- 用原型学习捕捉人类移动模式，结合扩散模型重建轨迹。
- 在FourSquare和WuXi数据集上准确率分别提升6.28%和2.52%。
- 适合数据缺失严重、仅能获取端点的轨迹补全场景。
轨迹数据对诸多应用至关重要，但常因设备限制和采集环境差异导致不完整。现有方法依赖稀疏轨迹或速度信息推断缺失点，假设稀疏轨迹保留关键行为模式，对数据采集要求高，并忽视大规模人类轨迹嵌入的潜力。为此，我们提出ProDiff，一种仅需两个端点作为最小信息的轨迹补全框架。该框架融合原型学习以嵌入人类运动模式，并采用去噪扩散概率模型实现鲁棒的时空重建。通过定制损失函数联合训练，确保有效补全。ProDiff在FourSquare和WuXi数据集上分别取得6.28%和2.52%的准确率提升。进一步分析显示生成轨迹与真实轨迹相关性达0.927，验证了方法有效性。
原文摘要 · Abstract (English)
Trajectory data is crucial for various applications but often suffers from incompleteness due to device limitations and diverse collection scenarios. Existing imputation methods rely on sparse trajectory or travel information, such as velocity, to infer missing points. However, these approaches assume that sparse trajectories retain essential behavioral patterns, which place significant demands on data acquisition and overlook the potential of large-scale human trajectory embeddings. To address this, we propose ProDiff, a trajectory imputation framework that uses only two endpoints as minimal information. It integrates prototype learning to embed human movement patterns and a denoising diffusion probabilistic model for robust spatiotemporal reconstruction. Joint training with a tailored loss function ensures effective imputation. ProDiff outperforms state-of-the-art methods, improving accuracy by 6.28\% on FourSquare and 2.52\% on WuXi. Further analysis shows a 0.927 correlation between generated and real trajectories, demonstrating the effectiveness of our approach.
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