Using Large Language Models to Analyze Interviews for Driver Psychological Assessment: A Performance Comparison of ChatGPT and Google-Gemini

Abstract

This study examines the application of large language models (LLMs) in analyzing subjective driver perceptions during tunnel driving simulations, comparing the effectiveness of questionnaires and interviews. Building on previous research involving driver simulations, we recruited 29 new participants, collected their perceptions via questionnaires, and conducted follow-up interviews. The interview data were analyzed using three LLMs: GPT-3.5, GPT-4, and Google-Gemini. The results revealed that while GPT-4 provides more in-depth and accurate analysis, it is significantly slower than GPT-3.5. Conversely, Google-Gemini demonstrated a balance between analysis quality and speed, outperforming the other models overall. Despite the challenge of occasional misunderstandings, LLMs still have the potential to enhance the efficiency and accuracy of subjective data analysis in transportation research.