Enhancing Travel Choice Modeling with Large Language Models: A Prompt-Learning Approach

TL;DR

This paper introduces a prompt-learning-based Large Language Model framework that enhances travel choice prediction accuracy and provides explicit explanations, addressing data limitations and explainability challenges in transportation modeling.

Contribution

It presents a novel LLM-based approach for travel choice modeling that outperforms existing methods and offers transparent, individual-level explanations.

Findings

LLM significantly outperforms traditional models in prediction accuracy

The framework provides clear, individual-level explanations

Effective with limited survey data

Abstract

Travel choice analysis is crucial for understanding individual travel behavior to develop appropriate transport policies and recommendation systems in Intelligent Transportation Systems (ITS). Despite extensive research, this domain faces two critical challenges: a) modeling with limited survey data, and b) simultaneously achieving high model explainability and accuracy. In this paper, we introduce a novel prompt-learning-based Large Language Model(LLM) framework that significantly improves prediction accuracy and provides explicit explanations for individual predictions. This framework involves three main steps: transforming input variables into textual form; building of demonstrations similar to the object, and applying these to a well-trained LLM. We tested the framework's efficacy using two widely used choice datasets: London Passenger Mode Choice (LPMC) and Optima-Mode collected in Switzerland. The results indicate that the LLM significantly outperforms state-of-the-art deep learning methods and discrete choice models in predicting people's choices. Additionally, we present a case of explanation illustrating how the LLM framework generates understandable and explicit explanations at the individual level.