Large Language Models Predict Transportation Mode Choice Behavior for a Variety of Alternative Sets

Mode choice models are important for investigating how travelers will react to changes in public transportation fares and the introduction of new mobility services. The models are essentially built for a given set of mode alternatives, for which parametric utility functions for each mode are defined, and the parameters are estimated using mode choice behavior data. Therefore, the models are dependent on the target mode alternatives in the modeling step and not generalizable to other modes. This study aimed to develop a general mode choice model that can be applied to various sets of mode alternatives. We used large language models to achieve generalizability. The model input comprised sentences that represented different alternatives and variables related to choosing a travel mode. The output was a word that indicated the mode that would be selected. In this study, we created a textual dataset based on four publicly available mode choice datasets. The experimental results showed that the proposed language-based mode choice model, our proposed approach, was more versatile than the classical multinomial logit model in predicting a variety of mode alternative sets.