arXiv：cs.LG（机器学习，全量分类）· Yidi Wang, Yunhe Zhang, Bangchao Deng, Dingqi Yang, Pengyang Wang·· 14 小时前AI 评分35
Nomad：面向无目标轨迹人类移动生成的可迁移框架
Relative Transitions, Not Absolute Destinations: A Transfer-and-Ground Framework for Target-Trajectory-Free Human Mobility Generation
AI 导读
研究者提出 Nomad，一个"迁移-落地"框架，用于在目标城市无轨迹数据条件下生成人类移动轨迹，仅依赖目标城市的 POI 坐标与类别。该方法用历史条件流匹配模型学习源城市中 POI 上下文间的相对转移先验，再通过行为图与探索-返回游走将采样转移落到目标 POI 地图上。在 10 座城市、14 次迁移实验中，Nomad 的分布保真度平均误差较最优基线降低约 15%，下游效用降低约 3%。
正文
Abstract:Individual mobility trajectories support urban analysis and location-based services, yet most trajectory generators require observations from their deployment city. This assumption excludes precisely the cities where trajectories are unavailable even though points of interest (POIs) and their attributes can be obtained from public maps. We study target-trajectory-free generation: learning from POIs and trajectories in source cities while utilizing only POI coordinates and categories in a target city, with no target trajectory or trajectory-derived statistic available for training, model selection, or generation. Existing trajectory generators typically predict absolute destinations, entangling reusable movement behavior with city-specific POI identities and spatial layouts. Our core insight is to replace this city-bound output with context-conditioned relative transitions. We propose Nomad, a transfer-and-ground framework that separates learning how people move from determining where those movements are realized. Specifically, a history-conditioned flow-matching model learns from source trajectories a transition prior over semantic displacement between POI contexts, geographic displacement, and elapsed time; at inference, a behavior graph and an exploration--return walk ground sampled transitions onto the target POI map. This factorization enables a direct test of representation level transferability without assuming invariance of the full mobility distribution. Extensive experiments across ten cities and 14 transfers show that Nomad outperforms adaptation baselines in trajectory fidelity and downstream utility, lowering the average error over the best baseline of each metric by about 15% in distributional fidelity and about 3% in downstream utility.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.02033 [cs.LG] |
| (or arXiv:2610.02033v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.02033 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yidi Wang [view email]
[v1]
Thu, 1 Oct 2026 16:49:30 UTC (1,477 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org