Mobility trajectory data provide essential support for smart city applications. However, such data are often difficult to obtain. Meanwhile, most existing trajectory generation methods implicitly assume that at least a subset of real mobility data from target city is available, which limits their applicability in data-inaccessible scenarios. In this work, we propose a new problem setting, called bus-conditioned zero-shot trajectory generation, where no mobility trajectories from a target city are accessible. The generation process relies solely on source city mobility data and publicly available bus timetables from both cities. Under this setting, we propose MobTA, the first approach to introduce task arithmetic into trajectory generation. MobTA models the parameter shift from bus-timetable-based trajectory generation to mobility trajectory generation in source city, and applies this shift to target city through arithmetic operations on task vectors. This enables trajectory generation that reflects target-city mobility patterns without requiring any real mobility data from it. Furthermore, we theoretically analyze MobTA's stability across base and instruction-tuned LLMs. Extensive experiments show that MobTA significantly outperforms existing methods, and achieves performance close to models finetuned using target city mobility trajectories.

翻译：移动轨迹数据为智慧城市应用提供了关键支撑。然而，此类数据往往难以获取。与此同时，现有的大多数轨迹生成方法都隐含地假设至少可以获得目标城市的部分真实移动数据，这限制了它们在数据不可访问场景下的适用性。在本研究中，我们提出了一种新的问题设定，称为基于公交时刻表的零样本轨迹生成，其中目标城市的任何移动轨迹均不可访问。生成过程仅依赖于源城市的移动数据和两个城市公开可用的公交时刻表。在此设定下，我们提出了MobTA，这是首个将任务算术引入轨迹生成的方法。MobTA在源城市中建模了从基于公交时刻表的轨迹生成到移动轨迹生成的参数偏移，并通过任务向量的算术运算将此偏移应用于目标城市。这使得生成的轨迹能够反映目标城市的移动模式，而无需其任何真实移动数据。此外，我们从理论上分析了MobTA在基础大语言模型和指令微调大语言模型上的稳定性。大量实验表明，MobTA显著优于现有方法，并且达到了接近使用目标城市移动轨迹进行微调的模型的性能。