构建真实时空约束的旅行规划数据集，提升大模型个性化行程生成能力。
TripCraft: A Benchmark for Spatio-Temporally Fine Grained Travel Planning
- 整合公交时刻表、活动可用性等真实约束，构建时空一致的旅行数据集。
- 提出五项连续评估指标，7天行程下用餐时间得分从61%提升至80%。
- 适合研究个性化智能旅行助手的开发者与研究人员使用。
近期大语言模型在个性化旅行规划中的潜力被广泛探索，但现有基准数据集仍受限于半合成数据、空间不一致性及关键约束缺失，难以支持实际应用。为弥补这些不足，本文提出TripCraft，一个融合真实世界约束的时空精细旅行规划数据集，包含公共交通时刻表、活动可用性、多样景点类别及用户画像，以增强个性化。为超越传统二元验证方法，我们设计了五项连续评估指标：时间用餐分、时间景点分、空间分、顺序分和人物画像分，全面衡量行程质量。在参数优化设置下，7天行程的用餐时间评分从61%提升至80%。TripCraft为大模型驱动的个性化旅行规划建立了新基准，提供更真实、约束感知的行程生成框架。数据集与代码将在论文录用后公开。
原文摘要 · Abstract (English)
Recent advancements in probing Large Language Models (LLMs) have explored their latent potential as personalized travel planning agents, yet existing benchmarks remain limited in real world applicability. Existing datasets, such as TravelPlanner and TravelPlanner+, suffer from semi synthetic data reliance, spatial inconsistencies, and a lack of key travel constraints, making them inadequate for practical itinerary generation. To address these gaps, we introduce TripCraft, a spatiotemporally coherent travel planning dataset that integrates real world constraints, including public transit schedules, event availability, diverse attraction categories, and user personas for enhanced personalization. To evaluate LLM generated plans beyond existing binary validation methods, we propose five continuous evaluation metrics, namely Temporal Meal Score, Temporal Attraction Score, Spatial Score, Ordering Score, and Persona Score which assess itinerary quality across multiple dimensions. Our parameter informed setting significantly enhances meal scheduling, improving the Temporal Meal Score from 61% to 80% in a 7 day scenario. TripCraft establishes a new benchmark for LLM driven personalized travel planning, offering a more realistic, constraint aware framework for itinerary generation. Dataset and Codebase will be made publicly available upon acceptance.
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