ZeroHAT: Behavior-Conditioned Zero-Shot Human Activity Trace Generation

arXiv:2609.20310v1 Announce Type: new Abstract: 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.

Abstract: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.

Machine Learning (cs.LG)

arXiv:2609.20310 [cs.LG]

(or arXiv:2609.20310v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.20310 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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ZeroHAT: Behavior-Conditioned Zero-Shot Human Activity Trace Generation

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