ZeroHAT：行为条件化的零样本人类活动轨迹生成
ZeroHAT: Behavior-Conditioned Zero-Shot Human Activity Trace Generation
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中文总结 AI 辅助
ZeroHAT提出行为条件化零样本框架，迁移源区域行为模式并适配目标区域上下文，生成合成人类活动轨迹，在十城市基准上实现下游效用4.5-6.4倍提升和保真度15.6%-40.8%改善。
中文摘要 AI 辅助
人类活动轨迹（HATs）记录了个体对兴趣点的时间戳访问，对于移动性预测和城市模拟等应用至关重要。然而，由于高收集成本和隐私问题，获取大规模HATs具有挑战性。合成HAT生成提供了一种使此类数据可用的有前景的方法，并已引起工业界和学术界的日益关注。尽管已有许多工作致力于此主题，但大多数依赖于某一地区的真实数据来生成同一地区的合成数据，这对于许多无法获得真实HATs的地区而言是不可行的。为填补这一空白，我们提出了ZeroHAT，一个行为条件化框架，通过迁移从源地区真实HATs中学习到的行为模式，并利用目标地区公开可用的上下文信息进行适配，以零样本方式为目标地区生成合成HATs。ZeroHAT包含三个关键的新颖组件：（i）多维一致性感知意图提取器；（ii）跨区域行为克隆模块；以及（iii）行为条件化活动实现模块。我们在一个十城市基准上评估了ZeroHAT，大量实验表明，ZeroHAT在归一化下游效用上达到最强基线的4.5-6.4倍，并在目标地区上将平均保真度提高了15.6%-40.8%。
英文摘要
Human activity traces record individuals' timestamped visits to points of interest and are essential for applications such as mobility prediction and urban simulation. However, accessing large-scale HATs is challenging due to high collection costs and privacy concerns. Synthetic HAT generation offers a promising way to make such data available and has attracted growing interest from both industry and academia. Although many efforts have been devoted to this topic, most of them rely on real data from a region to generate synthetic data for the same region, which is infeasible for the many regions where real HATs are unavailable. To fill this gap, we propose ZeroHAT, a behavior-conditioned framework that generates synthetic HATs for a target region in a zero-shot manner by transferring behavioral patterns learned from real HATs in source regions and adapting them with publicly available contextual information about the target region. ZeroHAT has three key novel components: (i) a multidimensional consistency-aware intent extractor; (ii) a cross-region behavioral cloning module; and (iii) a behavior-conditioned activity realization module. We evaluate ZeroHAT on a ten-city benchmark, where extensive experiments show that ZeroHAT achieves 4.5-6.4x the normalized downstream utility of the strongest baseline and improves average fidelity by 15.6%-40.8% across target regions.
