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 this https URL

本文介绍了利用大型语言模型（LLM）进行人类移动性预测的潜力。通过引入 LLM-Mob 方法，该研究提出了历史停留和上下文停留的概念，以捕捉人类移动性中的长期和短期依赖，并通过使用预测目标的时间信息实现时间感知预测。全面的评估结果表明，LLM-Mob 在提供准确可解释预测方面表现出色，突显了 LLM 在推进人类移动性预测技术上的未开发潜力。