基于公交时间表的零样本轨迹生成
Bus-Conditioned Zero-Shot Trajectory Generation via Task Arithmetic
AI总结：
本文提出MobTA方法，通过任务算术实现基于公交时间表的零样本轨迹生成，无需目标城市真实数据即可生成符合其移动模式的轨迹。
摘要：
移动轨迹数据为智慧城市应用提供了必要的支持。然而，此类数据往往难以获取。同时，大多数现有轨迹生成方法隐含假设至少有一个目标城市的实时移动数据子集可用，这限制了它们在数据不可获取场景中的适用性。在本文中，我们提出了一种新的问题设定，称为基于公交时间表的零样本轨迹生成，其中无法获取目标城市的移动轨迹。生成过程仅依赖于源城市移动数据和两个城市公开的公交时刻表。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模型了从基于公交时刻表的轨迹生成到源城市移动轨迹生成的参数转移，并通过在任务向量上进行算术运算将此转移应用于目标城市。这使轨迹生成能够反映目标城市移动模式，而无需任何真实移动数据。此外，我们还从理论上分析了MobTA在基础和指令微调LLM之间的稳定性。广泛的实验表明，MobTA显著优于现有方法，并实现了接近使用目标城市移动轨迹微调的模型的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
