Ye, Jiao (Shenzhen University), Yang, Yuwei (Nanjing University of Aeronautics and Astronautics), Qiu, Zhongxi (Southern University of Science and Technology), YANG, QI (Guangdong Laboratory of Artificial Intelligence and Digital Econ), Hu, Kai (School of Future Technology, South China University of Technolog), Chen, Jun (Southeast University), Shi, Enze (Southern University of Science and Technology)

Applying Pre-Trained Language Models on Multimodal Travel Behavior Prediction Using Small-Sample Survey Data

Scheduled for presentation during the Invited Session "S18a-Innovative Applications of LLM in Multimodal Transportation Systems" (TH-LM-T18), Thursday, November 20, 2025, 10:30−10:50, Southport 3

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

This investigation explores the application of pre-trained language models (PLMs) in predicting multimodal travel behavior using data derived from a stated preference (SP) survey. The study primarily examines the capacity of PLMs to assimilate complex linguistic inputs and their effectiveness in modeling transportation behaviors over various travel distances. By converting numerical data into textual information and utilizing the sophisticated processing capabilities of PLMs, particularly the BERT and DistilBERT models, this research outlines marked enhancements in predictive accuracy compared to three traditional numerical-based models. Comparative analysis of evaluation results emphasizes the superior performance of PLMs in managing complex classification tasks that involve textual data, demonstrating their effectiveness over traditional models in all evaluated metrics. This finding indicates that PLMs is highly proficient in managing complex decision-making scenarios in transportation research, presenting a potent alternative to conventional modeling techniques.