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.

翻译：本文首次提出面向城市漫步的开放域城市行程规划（OUIP）任务，该任务可直接基于用户自然语言描述的需求生成行程方案。与限制用户表达更细致需求、阻碍真正个性化的传统行程规划不同，OUIP实现了突破。近年来，大型语言模型（LLM）展现出处理多样化任务的潜力，但由于缺乏实时信息、知识不完整及空间感知能力不足，其在OUIP中无法独立提供令人满意的用户体验。为此，我们提出ItiNera系统，通过协同空间优化与大型语言模型为用户提供定制化城市行程规划服务。具体而言，我们开发了基于LLM的兴趣点特征提取与更新流水线，构建用户专属的个性化兴趣点数据库。针对每位用户需求，我们利用LLM协同基于嵌入的模块从用户兴趣点数据库中检索候选兴趣点，进而通过空间优化模块对这些兴趣点进行排序，最后由LLM生成个性化且具有空间连贯性的行程方案。据我们所知，本研究首次将LLM集成到行程规划方案中实现创新。离线数据集实验与在线主观评估结果表明，与现有基于LLM的方案相比，本系统能生成响应性更强、空间连贯性更佳的行程方案。该系统已在途途在线旅游服务平台投入生产，吸引数千用户用于城市旅游规划。