Large-scale human mobility simulation is critical for many science domains such as urban science, epidemiology, and transportation analysis. Recent works treat large language models (LLMs) as human agents to simulate realistic mobility trajectories by modeling individual-level cognitive processes. However, these approaches generate individual mobility trajectories independently, without any population-level coordination mechanism, and thus fail to capture the emergence of collective behaviors. To address this issue, we design M2LSimu, a mobility measures-guided multi-prompt adjustment framework that leverages mobility measures derived from shared data as guidance to refine individual-level prompts for realistic mobility generation. Our framework applies coarse-grained adjustment strategies guided by mobility measures, progressively enabling fine-grained individual-level adaptation while satisfying multiple population-level mobility objectives under a limited budget. Experiments show that M2LSimu significantly outperforms state-of-the-art LLM-based methods on two public datasets.

翻译：大规模人类移动仿真对于城市科学、流行病学和交通分析等多个科学领域至关重要。近期研究将大语言模型（LLM）视为人类智能体，通过建模个体层面的认知过程来模拟真实的移动轨迹。然而，这些方法独立生成个体移动轨迹，缺乏群体层面的协调机制，因而无法捕捉集体行为的涌现。为解决这一问题，我们设计了M2LSimu——一种移动性度量引导的多提示调整框架，该框架利用从共享数据中提取的移动性度量作为指导，优化个体层面的提示以生成真实的移动行为。我们的框架应用由移动性度量引导的粗粒度调整策略，在有限预算下逐步实现细粒度的个体层面适配，同时满足多个群体层面的移动目标。实验表明，在两个公开数据集上，M2LSimu显著优于当前最先进的基于LLM的方法。