Applying Large Language Models to Travel Satisfaction Analysis

• URL: http://arxiv.org/abs/2505.23262v2
• Date: Fri, 07 Nov 2025 14:09:42 GMT
• Title: Applying Large Language Models to Travel Satisfaction Analysis
• Authors: Pengfei Xu, Donggen Wang,
• Abstract summary: This study uses household survey data collected in Shanghai to identify the existence and source of misalignment between Large Language Models (LLMs) and humans.<n>LLMs have strongcapabilities in contextual understanding and generalization, significantly reducing dependence on task-specific data.<n>We propose an LLM-based modeling approach that can be applied to model travel behavior with small sample sizes.
• Score: 2.5105418815378555
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As a specific domain of subjective well-being, travel satisfaction has recently attracted much research attention. Previous studies primarily relied on statistical models and, more recently, machine learning models to explore its determinants. Both approaches,however, depend on sufficiently large sample sizes and appropriate statistical assumptions. The emergence of Large Language Models (LLMs) offers a new modeling approach that can address these limitations. Pre-trained on extensive datasets, LLMs have strongcapabilities in contextual understanding and generalization, significantly reducing their dependence on task-specific data and stringent statistical assumptions. The main challenge in applying LLMs lies in the behavioral misalignment between LLMs and humans. Using household survey data collected in Shanghai, this study identifies the existence and source of misalignment, and applies a few-shot learning method to address the misalignment issue. We find that the zero-shot LLM exhibits behavioral misalignment, leading to low prediction accuracy. With just a few samples, few-shot learning can align LLMs and enable them to outperform baseline models. Discrepancies in variable importance among machine learning model, zero-shot LLM, and few-shot LLM reveal that the misalignment arises from the gap between the general knowledge embedded in pre-trained LLMs and the specific, unique characteristics of the dataset. On these bases, we propose an LLM-based modeling approach that can be applied to model travel behavior with small sample sizes. This study highlights the potential of LLMs for modeling not only travel satisfaction but also broader aspects of travel behavior.

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