Public events, such as concerts and sports games, can be major attractors for large crowds, leading to irregular surges in travel demand. Accurate human mobility prediction for public events is thus crucial for event planning as well as traffic or crowd management. While rich textual descriptions about public events are commonly available from online sources, it is challenging to encode such information in statistical or machine learning models. Existing methods are generally limited in incorporating textual information, handling data sparsity, or providing rationales for their predictions. To address these challenges, we introduce a framework for human mobility prediction under public events (LLM-MPE) based on Large Language Models (LLMs), leveraging their unprecedented ability to process textual data, learn from minimal examples, and generate human-readable explanations. Specifically, LLM-MPE first transforms raw, unstructured event descriptions from online sources into a standardized format, and then segments historical mobility data into regular and event-related components. A prompting strategy is designed to direct LLMs in making and rationalizing demand predictions considering historical mobility and event features. A case study is conducted for Barclays Center in New York City, based on publicly available event information and taxi trip data. Results show that LLM-MPE surpasses traditional models, particularly on event days, with textual data significantly enhancing its accuracy. Furthermore, LLM-MPE offers interpretable insights into its predictions. Despite the great potential of LLMs, we also identify key challenges including misinformation and high costs that remain barriers to their broader adoption in large-scale human mobility analysis.

翻译：公共事件（如音乐会、体育赛事）往往成为大规模人群的聚集诱因，导致出行需求出现不规则激增。因此，针对公共事件开展精准的人类移动性预测，对活动规划及交通或人流管理至关重要。尽管在线渠道通常提供丰富的公共事件文本描述，但在统计模型或机器学习模型中编码此类信息仍具挑战性。现有方法普遍在整合文本信息、处理数据稀疏性或为其预测提供理论依据方面存在局限。为应对这些挑战，我们提出了一种基于大语言模型（LLMs）的公共事件下人类移动性预测框架（LLM-MPE），利用其处理文本数据、从少量样本中学习以及生成人类可读解释的非凡能力。具体而言，LLM-MPE首先将来自在线渠道的原始、非结构化工件描述转换为标准化格式，然后将历史移动数据划分为常规组件和事件相关组件。我们设计了一种提示策略，引导LLMs在考虑历史移动特性和事件特征的基础上，进行需求预测并提供合理解释。基于纽约巴克莱中心的公开事件信息与出租车出行数据开展案例研究。结果表明，LLM-MPE超越传统模型，尤其在活动日表现突出，其中文本数据显著提升了其准确性。此外，LLM-MPE为其预测提供可解释的洞见。尽管LLMs潜力巨大，我们仍识别出包括虚假信息和高成本在内的关键挑战，这些仍是其在大规模人类移动分析领域广泛推广的障碍。