用Transformer改进轨迹生成，更准更细。
Traj-Transformer: Diffusion Models with Transformer for GPS Trajectory Generation
- 用Transformer替代卷积模型做噪声预测
- 在两个真实数据集上生成轨迹更准确
- 适合需要高精度路径模拟的研究者
GPS设备的普及推动了时空数据挖掘的发展，使机器学习模型能够模拟人类决策并生成真实轨迹，既降低数据采集成本，又保护隐私。近期研究显示扩散模型在高质量轨迹生成方面具有潜力。然而，现有方法多依赖基于卷积的架构（如UNet）进行扩散过程中的噪声预测，常因模型容量有限导致显著偏差和街道级细节丢失。本文提出轨迹Transformer，采用Transformer主干网络同时完成条件信息嵌入与噪声预测。探索了位置嵌入与经度-纬度嵌入两种坐标编码策略，并分析模型在不同尺度下的表现。在两个真实世界数据集上的实验表明，轨迹Transformer显著提升了生成质量，有效缓解了先前方法中的偏差问题。
原文摘要 · Abstract (English)
The widespread use of GPS devices has driven advances in spatiotemporal data mining, enabling machine learning models to simulate human decision making and generate realistic trajectories, addressing both data collection costs and privacy concerns. Recent studies have shown the promise of diffusion models for high-quality trajectory generation. However, most existing methods rely on convolution based architectures (e.g. UNet) to predict noise during the diffusion process, which often results in notable deviations and the loss of fine-grained street-level details due to limited model capacity. In this paper, we propose Trajectory Transformer, a novel model that employs a transformer backbone for both conditional information embedding and noise prediction. We explore two GPS coordinate embedding strategies, location embedding and longitude-latitude embedding, and analyze model performance at different scales. Experiments on two real-world datasets demonstrate that Trajectory Transformer significantly enhances generation quality and effectively alleviates the deviation issues observed in prior approaches.
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