Large Language Models for Travel Behavior Prediction

Understand

Travel behavior prediction is a core problem in transportation demand management and is traditionally addressed using numerical models calibrated on observed data.

• With recent advances in large language models (LLMs), new opportunities have emerged to model human decision-making through natural language reasoning.
• This study explores the use of LLMs for travel behavior prediction through two complementary frameworks.
• The first framework employs a zero-shot prompting strategy, where the prediction task, traveler attributes, and relevant domain knowledge are described in text, enabling the LLM to directly generate predictions without task-specific training data.