Aligning LLM with human travel choices: a persona-based embedding learning approach
This work addresses the challenge of behavioral misalignment in LLMs for travel demand modeling, offering a more efficient and interpretable method for practitioners, though it is incremental as it builds on existing alignment techniques.
The paper tackles the problem of aligning large language models (LLMs) with human travel choices to improve travel demand modeling, introducing a persona-based embedding learning approach that significantly outperforms baseline models in predicting mode choice shares and individual outcomes on the Swissmetro dataset.
The advent of large language models (LLMs) presents new opportunities for travel demand modeling. However, behavioral misalignment between LLMs and humans presents obstacles for the usage of LLMs, and existing alignment methods are frequently inefficient or impractical given the constraints of typical travel demand data. This paper introduces a novel framework for aligning LLMs with human travel choice behavior, tailored to the current travel demand data sources. Our framework uses a persona inference and loading process to condition LLMs with suitable prompts to enhance alignment. The inference step establishes a set of base personas from empirical data, and a learned persona loading function driven by behavioral embeddings guides the loading process. We validate our framework on the Swissmetro mode choice dataset, and the results show that our proposed approach significantly outperformed baseline choice models and LLM-based simulation models in predicting both aggregate mode choice shares and individual choice outcomes. Furthermore, we showcase that our framework can generate insights on population behavior through interpretable parameters. Overall, our research offers a more adaptable, interpretable, and resource-efficient pathway to robust LLM-based travel behavior simulation, paving the way to integrate LLMs into travel demand modeling practice in the future.