I ti N era: Integrating Spatial Optimization with Large Language Models for Open-domain Urban Itinerary Planning (2024)

Abstract: 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.

## introduction as a novel form of urban travel, citywalk (germano, 2023) invites travelers to wander through city streets and immerse themselves in local culture, offering a more dynamic, immersive, and finegrained travel experience compared to traditional tourism. planning a citywalk is a complex urban itinerary planning problem @xcite , involving travel-related information gathering, poi selection, route mapping, and customization for diverse user needs. specifically, citywalk differs from traditional tourism by (1) dynamic information: involving rapidly changing pois and needing up-to-date information on temporary events, (2) * equal contribution. corresponding authors. personalization: prioritizing individual preferences over widely recognized pois, and (3) diverse constraints: considering complex constraints like personal interests and accessibility requirements. an example of the ouip problem is shown in fig. 1 . existing itinerary planning studies focus on traditional tourism. they consider coarse-grained user requirements such as geographical constraints @xcite and time budgets @xcite to improve the quality of an itinerary @xcite . while these optimization-based approaches maintain the quality of pois and spatial coherency, they struggle to address dynamic and detailed personal demands, leading to itineraries that lack personalization and diversity. recently, large language models (llms) (ope-nai, 2023) have shown impressive applications in understanding user needs and following instructions. however, their limitations in itinerary planning are evident @xcite : @xcite pure llms cannot refer to specific poi lists, resulting in outdated or hallucinated pois. (2) llms lack the optimization capabilities required for planning tasks, leading to suboptimal itineraries. consequently, llm-generated itineraries can be circuitous, lack detail, and include impractical information. to address these limitations, in this work, we first define the open-domain urban itinerary planning (ouip) problem, which involves generating personalized travel itineraries based on user requests in natural language. then, we propose itinera, a holistic ouip system that integrates spatial optimization with llms. itinera comprises five llm-assisted modules: user-owned poi database construction (upc), request decomposition (rd), preference-aware poi retrieval (ppr), cluster-aware spatial optimization (cso), and itinerary generation (ig), to deliver personalized and spatially coherent itineraries. our overall contributions are: • we introduce the ouip problem to provide personalized urban travel itineraries based on users' natural language inputs and propose metrics to measure the quality of generated itineraries. ## related work llms in urban applications since chatgpt, llms have demonstrated strong knowledge and reasoning capabilities. recent studies highlight the potential of llms in urban data processing @xcite and urban planning @xcite . these works reveal llms' capabilities in predicting human mobility patterns @xcite and emphasize their predictive strength @xcite . in transportation, llms contribute to traffic safety analysis @xcite , enhance traffic forecasting @xcite , and automate accident report generation @xcite , showing their applicability in urban transportation. leveraging llms for travel planning has recently gained public interest. travelplanner @xcite proposes a sandbox environment with various tools for benchmark-ing llms on multi-day travel planning, revealing llms' current limitations for complex planning tasks. unlike travelplanner, our system focuses on fine-grained ouip, addressing urban itinerary planning within a single day, but can be seamlessly extended to multi-day travel planning. ## methodology we formalize the ouip problem and explain how itinera generates itineraries, as shown in fig. 2 . ## conclusion we introduce the ouip problem and a solution itinera that integrates llms with spatial optimization. itinera enables the generation of personalized and spatially coherent itineraries directly from natural language requests. experiments on the real-world dataset and deployed system performance validate the effectiveness of our approach. this study not only sets a new benchmark for itinerary planning technologies but also broadens venues for further innovations in leveraging llms for complex problem-solving in urban contexts.