As global tourism expands and artificial intelligence technology advances, intelligent travel planning services have emerged as a significant research focus. Within dynamic real-world travel scenarios with multi-dimensional constraints, services that support users in automatically creating practical and customized travel itineraries must address three key objectives: Rationality, Comprehensiveness, and Personalization. However, existing systems with rule-based combinations or LLM-based planning methods struggle to fully satisfy these criteria. To overcome the challenges, we introduce TravelAgent, a travel planning system powered by large language models (LLMs) designed to provide reasonable, comprehensive, and personalized travel itineraries grounded in dynamic scenarios. TravelAgent comprises four modules: Tool-usage, Recommendation, Planning, and Memory Module. We evaluate TravelAgent's performance with human and simulated users, demonstrating its overall effectiveness in three criteria and confirming the accuracy of personalized recommendations.

翻译：随着全球旅游业的发展和人工智能技术的进步，智能旅行规划服务已成为重要的研究热点。在具有多维约束的动态现实旅行场景中，支持用户自动创建实用且定制化旅行行程的服务必须满足三个关键目标：合理性、全面性和个性化。然而，现有基于规则组合或基于大语言模型（LLM）的规划方法难以完全满足这些标准。为应对这些挑战，我们提出了TravelAgent——一个由大语言模型驱动的旅行规划系统，旨在基于动态场景提供合理、全面且个性化的旅行行程。TravelAgent包含四个模块：工具使用模块、推荐模块、规划模块和记忆模块。我们通过人类用户和模拟用户评估了TravelAgent的性能，证明了其在三个标准上的整体有效性，并验证了个性化推荐的准确性。