Large Language Models for Travel Behavior Prediction
Baichuan Mo Hanyong Xu Dingyi Zhuang Ruoyun Ma Xiaotong Guo Jinhua Zhao
Travel behavior prediction is a fundamental task in transportation demand
management. The conventional methods for travel behavior prediction rely on
numerical data to construct mathematical models and calibrate model parameters
to represent human preferences. Recent advancement in large language models
(LLMs) has shown great reasoning abilities to solve complex problems. In this
study, we propose to use LLMs to predict travel behavior with prompt
engineering without data-based parameter learning. Specifically, we carefully
design our prompts that include 1) task description, 2) travel characteristics,
3) individual attributes, and 4) guides of thinking with domain knowledge, and
ask the LLMs to predict an individual's travel behavior and explain the
results. We select the travel mode choice task as a case study. Results show
that, though no training samples are provided, LLM-based predictions have
competitive accuracy and F1-score as canonical supervised learning methods such
as multinomial logit, random forest, and neural networks. LLMs can also output
reasons that support their prediction. However, though in most of the cases,
the output explanations are reasonable, we still observe cases that violate
logic or with hallucinations.