Understanding traveler behavior and accurately predicting travel mode choice are at the heart of transportation planning and policy-making. This study proposes TransMode-LLM, an innovative framework that integrates statistical methods with LLM-based techniques to predict travel modes from travel survey data. The framework operates through three phases: (1) statistical analysis identifies key behavioral features, (2) natural language encoding transforms structured data into contextual descriptions, and (3) LLM adaptation predicts travel mode through multiple learning paradigms including zero-shot and one/few-shot learning and domain-enhanced prompting. We evaluate TransMode-LLM using both general-purpose models (GPT-4o, GPT-4o-mini) and reasoning-focused models (o3-mini, o4-mini) with varying sample sizes on real-world travel survey data. Extensive experiment results demonstrate that the LLM-based approach achieves competitive accuracy compared to state-of-the-art baseline classifiers models. Moreover, few-shot learning significantly improves prediction accuracy, with models like o3-mini showing consistent improvements of up to 42.9\% with 5 provided examples. However, domain-enhanced prompting shows divergent effects across LLM architectures. In detail, it is helpful to improve performance for general-purpose models with GPT-4o achieving improvements of 2.27% to 12.50%. However, for reasoning-oriented models (o3-mini, o4-mini), domain knowledge enhancement does not universally improve performance. This study advances the application of LLMs in travel behavior modeling, providing promising and valuable insights for both academic research and transportation policy-making in the future.

翻译：理解出行者行为并准确预测出行方式选择是交通规划与政策制定的核心。本研究提出了TransMode-LLM，一个将统计方法与基于大语言模型（LLM）的技术相结合的创新框架，用于根据出行调查数据预测出行方式。该框架通过三个阶段运行：（1）统计分析识别关键行为特征，（2）自然语言编码将结构化数据转化为上下文描述，（3）LLM适配通过包括零样本、单样本/少样本学习以及领域增强提示在内的多种学习范式来预测出行方式。我们在真实世界的出行调查数据上，使用通用模型（GPT-4o, GPT-4o-mini）和侧重推理的模型（o3-mini, o4-mini），并采用不同样本量对TransMode-LLM进行了评估。大量实验结果表明，基于LLM的方法相较于最先进的基线分类器模型，达到了具有竞争力的准确率。此外，少样本学习显著提高了预测准确率，例如o3-mini模型在提供5个示例的情况下，表现出高达42.9%的持续提升。然而，领域增强提示在不同LLM架构间表现出不同的效果。具体而言，它有助于提升通用模型的性能，例如GPT-4o实现了2.27%到12.50%的改进。然而，对于面向推理的模型（o3-mini, o4-mini），领域知识增强并未普遍提升其性能。本研究推进了LLM在出行行为建模中的应用，为未来的学术研究和交通政策制定提供了有前景且有价值的见解。