Fine-tuning LLMs for Tourist Trajectory Prediction using Field Experiment Data

Recent research demonstrates a novel application of fine-tuned Large Language Models (LLMs) for predicting tourist behavior, specifically trajectories within a destination. This method addresses limitations of traditional prediction models by leveraging LLMs' capacity for integrating heterogeneous contextual data and common-sense reasoning, adapting general knowledge to specific local patterns. Validation at Wakayama Castle Park, Japan, indicates the effectiveness of this approach.

This development is crucial for entities involved in urban planning, tourism management, and infrastructure development, as it offers a sophisticated tool for anticipating visitor movements. Accurate prediction enables proactive resource allocation, optimized visitor flow, and more effective interventions, thereby enhancing operational efficiency and visitor experience within specific sites or broader regions.

Traditional methods for predicting tourist behavior struggle to generalize due to the context-dependent nature of visitor decisions (e.g., weather, fatigue).

Forward consideration, not a verified fact.