A Large Language Model-Driven Intelligent Route Planning Framework for Personalized Tourism Navigation in Scenic Areas

Abstract

A tourist may describe a desired day as 'relaxed, coastal, suitable for parents, and not too crowded,' whereas a route optimizer requires numerical attributes and explicit constraints. This paper connects these two representations without asking a large language model (LLM) to draw the route itself. The LLM parses a natural-language request into a preference profile; a semantic-spatial network then links that profile to attraction attributes, travel connections, visit durations, and congestion information. Route selection is performed by a multi-criteria model that evaluates preference fit together with distance and time costs. The framework is examined using six attractions in Dalian and four traveler profiles. Compared with the shortest-path baseline, the LLM-assisted method increases the reported preference-matching degree by about 29.1%, although it does not always return the minimum-distance itinerary. The result suggests a practical division of labor: language modeling handles ambiguous user intent, while an explicit optimizer remains responsible for spatial feasibility and resource limits.