Recent advancements in probing Large Language Models (LLMs) have explored their latent potential as personalized travel planning agents, though this remains a rather nascent field. Existing benchmarks, such as TravelPlanner and TravelPlanner+, rely on semi-synthetic data as well ignoring several key components of travel planning, limiting their real-world applicability. Therefore, we introduce TripCraft, a spatio-temporally coherent travel planning dataset incorporating real-world constraints, including public transit schedules, public events, varied attraction categories, and user personas for enhanced personalization. Our dataset enables more detailed trip itinerary generation (including duration spent at each point of interest based on users’ persona, transit between two points of interest, etc.) while ensuring spatio-temporal consistency. Further, we propose novel evaluation metrics (temporal meal score, attraction score, spatial score, ordering score, and persona score) to assess LLM-generated plans across temporal, spatial, sequential, and personal dimensions, overcoming the limitations of commonsense and hard constraint metrics. Interestingly, our parameter-informed setting significantly enhances meal scheduling, improving performance from 61% to 80% in the 7-day scenario- as quantified by a 19% gain in our temporal meal score. Moreover, TripCraft serves as a high-quality benchmark for advancing personalized LLM-driven travel planning.