Accurate human mobility prediction underpins many important applications across a variety of domains, including epidemic modelling, transport planning, and emergency responses. Due to the sparsity of mobility data and the stochastic nature of people's daily activities, achieving precise predictions of people's locations remains a challenge. While recently developed large language models (LLMs) have demonstrated superior performance across numerous language-related tasks, their applicability to human mobility studies remains unexplored. Addressing this gap, this article delves into the potential of LLMs for human mobility prediction tasks. We introduce a novel method, LLM-Mob, which leverages the language understanding and reasoning capabilities of LLMs for analysing human mobility data. We present concepts of historical stays and context stays to capture both long-term and short-term dependencies in human movement and enable time-aware prediction by using time information of the prediction target. Additionally, we design context-inclusive prompts that enable LLMs to generate more accurate predictions. Comprehensive evaluations of our method reveal that LLM-Mob excels in providing accurate and interpretable predictions, highlighting the untapped potential of LLMs in advancing human mobility prediction techniques. We posit that our research marks a significant paradigm shift in human mobility modelling, transitioning from building complex domain-specific models to harnessing general-purpose LLMs that yield accurate predictions through language instructions. The code for this work is available at https://github.com/xlwang233/LLM-Mob.

翻译：准确的人类移动性预测支撑着流行病建模、交通规划和应急响应等多个领域的重要应用。由于移动数据的稀疏性和人们日常活动的随机性，实现对位置信息的精确预测仍然是一项挑战。尽管最近开发的大型语言模型（LLMs）在众多语言相关任务中展现出卓越性能，但其在人类移动性研究中的适用性尚未被探索。针对这一空白，本文深入探讨了LLMs在人类移动性预测任务中的潜力。我们提出了一种名为LLM-Mob的新方法，该方法利用LLMs的语言理解与推理能力来分析人类移动数据。我们引入了历史停留和情境停留的概念，以捕捉人类移动中的长期与短期依赖性，并通过使用预测目标的时间信息实现时间感知预测。此外，我们设计了包含情境的提示，使LLMs能够生成更准确的预测。对我们方法的全面评估显示，LLM-Mob在提供准确且可解释的预测方面表现优异，凸显了LLMs在推进人类移动性预测技术中尚未被开发的潜力。我们认为，我们的研究标志着人类移动性建模的一次重大范式转变，即从构建复杂的领域特定模型转向利用通用LLMs通过语言指令生成精确预测。本工作的代码已在https://github.com/xlwang233/LLM-Mob公开。