个体移动轨迹支持城市分析和基于位置的服务，然而大多数轨迹生成器需要部署城市的观测数据。这一假设排除了那些无法获取轨迹数据但可从公共地图获得兴趣点（POI）及其属性的城市。我们研究无目标轨迹生成：从源城市的 POI 和轨迹中学习，同时在目标城市仅利用 POI 坐标和类别，且在训练、模型选择或生成过程中不使用任何目标轨迹或轨迹衍生统计量。现有的轨迹生成器通常预测绝对目的地，这将可复用的移动行为与特定城市的 POI 身份及空间布局纠缠在一起。我们的核心见解是用上下文条件化的相对转移取代这种受限于城市的输出。我们提出了 Nomad，一个将“学习人们如何移动”与“确定这些移动在何处实现”分离的迁移与落地框架。具体而言，一个历史条件化的流匹配模型从源轨迹中学习关于 POI 上下文间语义位移、地理位移和经过时间的转移先验；在推理阶段，通过行为图和探索-返回游走将采样的转移落地到目标 POI 地图上。这种分解使得能够直接测试表示层面的可迁移性，而无需假设完整移动分布的不变性。在十个城市和 14 次迁移任务中进行的大量实验表明，Nomad 在轨迹保真度和下游效用方面优于适应基线方法，其在分布保真度上比各指标最佳基线的平均误差降低约 15%，在下游效用方面降低约 3%。

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.