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

Abstract:Evaluating mobility interventions at tourist destinations requires predicting visitor behavior under varying conditions. Traditional methods struggle because tourist decisions depend heavily on context like weather and fatigue, yet models cannot generalize to unobserved scenarios. Large Language Models offer a solution by encoding commonsense knowledge about human behavior from pretraining, enabling reasoning about context-dependent decisions, while natural language representation flexibly integrates heterogeneous information. Fine-tuning on local trajectories adapts this general understanding to destination-specific patterns. We validate this approach using 566 trajectories from Wakayama Castle Park, Japan. Our fine-tuned Llama-3.1-8B achieves 49.1% next POI accuracy and maintains strong performance on undersampled scenarios like rainy days, demonstrating effective generalization. This establishes LLMs as high-fidelity behavior models for context-dependent tourist prediction, providing groundwork for counterfactual analysis of mobility interventions.

5 pages, 4 figures. Accepted at the 2nd Workshop on AI for Urban Planning (AI4UP) at AAAI-26, Singapore, January 2026

Computers and Society (cs.CY); Machine Learning (cs.LG)

arXiv:2608.20830 [cs.CY]

(or arXiv:2608.20830v1 [cs.CY] for this version)

https://doi.org/10.48550/arXiv.2608.20830 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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