  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.
