  The powerful reasoning capabilities of large language models (LLMs) have
brought revolutionary changes to many fields, but their performance in human
behaviour generation has not yet been extensively explored. This gap likely
emerges because the internal processes governing behavioral intentions cannot
be solely explained by abstract reasoning. Instead, they are also influenced by
a multitude of factors, including social norms and personal preference.
Inspired by the Theory of Planned Behaviour (TPB), we develop a LLM workflow
named Chain-of-Planned Behaviour (CoPB) for mobility behaviour generation,
which reflects the important spatio-temporal dynamics of human activities.
Through exploiting the cognitive structures of attitude, subjective norms, and
perceived behaviour control in TPB, CoPB significantly enhance the ability of
LLMs to reason the intention of next movement. Specifically, CoPB substantially
reduces the error rate of mobility intention generation from 57.8% to 19.4%. To
improve the scalability of the proposed CoPB workflow, we further explore the
synergy between LLMs and mechanistic models. We find mechanistic mobility
models, such as gravity model, can effectively map mobility intentions to
physical mobility behaviours. The strategy of integrating CoPB with gravity
model can reduce the token cost by 97.7% and achieve better performance
simultaneously. Besides, the proposed CoPB workflow can facilitate GPT-4-turbo
to automatically generate high quality labels for mobility behavior reasoning.
We show such labels can be leveraged to fine-tune the smaller-scale, open
source LLaMA 3-8B, which significantly reduces usage costs without sacrificing
the quality of the generated behaviours.
