TripCraft: A Benchmark for Spatio-Temporally Fine Grained Travel Planning

Recent studies on probing Large Language Models (LLMs) have explored their latent potential as personalized travel planning agents, yet existing benchmarks remain limited in realworld 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 spatio-temporally 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 1 establishes a new benchmark for LLM-driven personalized travel planning, offering a more realistic, constraint-aware framework for itinerary generation.