TourismMinds: A Geo-augmented LLM Framework for Semantic-aware Trajectory Analytics and Generation

With the rise of smart cities and digital tourism, understanding tourist movement is essential for enhancing personalized experiences and informing business decisions. Traditional methods struggle to integrate multi-source data and interpret complex behaviors, while large language models (LLMs) offer untapped potential. This study proposes an LLM-driven “Perception-Modeling-Generation” framework, validated by a Kulangsu smart tourism case. First, a five-stage spatiotemporal-semantic alignment pipeline integrates GPS, social media, POI, and weather data. Second, to address LLM limitations, we introduce a knowledge-enhanced framework combining geospatially-aware LoRA fine-tuning and a hierarchical Retrieval-Augmented Generation (RAG) mechanism. This approach reduces hallucinations by 15% and boosts geographic reasoning accuracy, achieving state-of-the-art intent recognition (15% gain) and landmark identification (72% accuracy). Third, we design a reprogramming strategy with geo-semantic constraints for generating spatially plausible, personalized trajectories in data-scarce settings, showing 89% similarity to real-world data. Supported by the Kulangsu Smart Tourism System, our method provides a scalable paradigm for spatiotemporal modeling and synthetic trajectory generation, benefiting personalized services and decision-making.