Where Would I Go Next? Large Language Models as Human Mobility Predictors
CoRR(2023)
摘要
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.
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关键词
large language models,human mobility
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