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arXiv · 2602.19694

All Cities are Equal: A Unified Human Mobility Generation Model Enabled by LLMs

Bo Liu·Tong Li·Zhu Xiao·Ruihui Li·Geyong Min·Zhuo Tang·Kenli Li
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Abstract

Synthetic human mobility generation is gaining traction as an ethical and practical approach to supporting the data needs of intelligent urban systems. Existing methods perform well primarily in data-rich cities, while their effectiveness declines significantly in cities with limited data resources. However, the ability to generate reliable human mobility data should not depend on a city's size or available resources, all cities deserve equal consideration. To address this open issue, we propose UniMob, a unified human mobility generation model across cities. UniMob is composed of three main components: an LLM-powered travel planner that derives high-level, temporally-aware, and semantically meaningful travel plans; a unified spatial embedding module that projects the spatial regions of various cities into a shared representation space; and a diffusion-based mobility generator that captures the joint spatiotemporal characteristics of human movement, guided by the derived travel plans. We evaluate UniMob extensively using two real-world datasets covering five cities. Comprehensive experiments show that UniMob significantly outperforms state-of-the-art baselines, achieving improvements of over 30\% across multiple evaluation metrics. Further analysis demonstrates UniMob's robustness in both zero- and few-shot scenarios, underlines the importance of LLM guidance, verifies its privacy-preserving nature, and showcases its applicability for downstream tasks.

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Bo Liu, Tong Li, Zhu Xiao, Ruihui Li, Geyong Min, Zhuo Tang, Kenli Li. 2026-02-23. All Cities are Equal: A Unified Human Mobility Generation Model Enabled by LLMs. https://arxiv.org/abs/2602.19694

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