Synergizing Spatial Optimization with Large Language Models for
  Open-Domain Urban Itinerary Planning
Yihong Tang Zhaokai Wang Ao Qu Yihao Yan Kebing Hou Dingyi Zhuang Xiaotong Guo Jinhua Zhao Zhan Zhao Wei Ma
In this paper, we for the first time propose the task of Open-domain Urban
Itinerary Planning (OUIP) for citywalk, which directly generates itineraries
based on users' requests described in natural language. OUIP is different from
conventional itinerary planning, which limits users from expressing more
detailed needs and hinders true personalization. Recently, large language
models (LLMs) have shown potential in handling diverse tasks. However, due to
non-real-time information, incomplete knowledge, and insufficient spatial
awareness, they are unable to independently deliver a satisfactory user
experience in OUIP. Given this, we present ItiNera, an OUIP system that
synergizes spatial optimization with Large Language Models (LLMs) to provide
services that customize urban itineraries based on users' needs. Specifically,
we develop an LLM-based pipeline for extracting and updating POI features to
create a user-owned personalized POI database. For each user request, we
leverage LLM in cooperation with an embedding-based module for retrieving
candidate POIs from the user's POI database. Then, a spatial optimization
module is used to order these POIs, followed by LLM crafting a personalized,
spatially coherent itinerary. To the best of our knowledge, this study marks
the first integration of LLMs to innovate itinerary planning solutions.
Extensive experiments on offline datasets and online subjective evaluation have
demonstrated the capacities of our system to deliver more responsive and
spatially coherent itineraries than current LLM-based solutions. Our system has
been deployed in production at the TuTu online travel service and has attracted
thousands of users for their urban travel planning.