  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.
