ITINERA: Integrating Spatial Optimization with Large Language Models for Open-domain Urban Itinerary Planning
This addresses the need for genuine personalization in urban travel planning, such as Citywalk, by providing a novel system for users seeking tailored itineraries, though it appears incremental as it builds on existing methods like spatial optimization and LLMs.
The paper tackles the problem of generating personalized urban itineraries from natural language requests by introducing the Open-domain Urban Itinerary Planning (OUIP) task and presenting ITINERA, a system that integrates spatial optimization with large language models to deliver customized and spatially coherent itineraries.
Citywalk, a recently popular form of urban travel, requires genuine personalization and understanding of fine-grained requests compared to traditional itinerary planning. In this paper, we introduce the novel task of Open-domain Urban Itinerary Planning (OUIP), which generates personalized urban itineraries from user requests in natural language. We then present ITINERA, an OUIP system that integrates spatial optimization with large language models to provide customized urban itineraries based on user needs. This involves decomposing user requests, selecting candidate points of interest (POIs), ordering the POIs based on cluster-aware spatial optimization, and generating the itinerary. Experiments on real-world datasets and the performance of the deployed system demonstrate our system's capacity to deliver personalized and spatially coherent itineraries compared to current solutions. Source codes of ITINERA are available at https://github.com/YihongT/ITINERA.