TravelAgent: An AI Assistant for Personalized Travel Planning
Aili Chen, Xuyang Ge, Ziquan Fu, Yanghua Xiao, Jiangjie Chen

TL;DR
TravelAgent is an AI-powered travel planning system utilizing large language models to generate personalized, comprehensive, and practical itineraries in dynamic scenarios, outperforming rule-based and LLM-based existing methods.
Contribution
The paper introduces TravelAgent, a novel LLM-based travel planning system with four modules that effectively addresses rationality, comprehensiveness, and personalization in dynamic travel scenarios.
Findings
TravelAgent achieves high effectiveness in personalized recommendations.
The system outperforms existing methods in key travel planning criteria.
Evaluation confirms the accuracy and practicality of generated itineraries.
Abstract
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…
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Taxonomy
TopicsData Management and Algorithms · Semantic Web and Ontologies · Transportation and Mobility Innovations
MethodsEmirates Airlines Office in Dubai
