Synergizing Spatial Optimization with Large Language Models for Open-Domain Urban Itinerary Planning

Massachusetts Institute of Technology, Aug. 2023 ~ Feb. 2024
Advisor: Prof. Jinhua Zhao
Publication: KDD Urban Computing Workshop Resources: arxiv

In this paper, we introduce Open-domain Urban Itinerary Planning (OUIP) for city walks, a new approach that generates itineraries based on natural language user requests, unlike conventional methods that limit personalization. Our system, ItiNera, combines spatial optimization with large language models (LLMs) to customize urban itineraries. ItiNera uses an LLM pipeline to create and update a personalized database of points of interest (POIs) and a spatial optimization module to organize these POIs into a coherent itinerary. Experiments show ItiNera outperforms current LLM-based solutions in creating responsive and coherent itineraries. It is now used in the TuTu online travel service, attracting thousands of users.