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

arXiv: https://arxiv.org/abs/2502.20508 · HTML: https://mlsanity.com/paper/2502.20508/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 21 references that are in the map.
- First posted on arXiv 27 Feb 2025.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models](https://mlsanity.com/paper/2212.04088/) (2022)
- [TravelPlanner: A Benchmark for Real-World Planning with Language Agents](https://mlsanity.com/paper/2402.01622/) (2024)
- [Large Language Models as Commonsense Knowledge for Large-Scale Task Planning](https://mlsanity.com/paper/2305.14078/) (2023)
- [ADaPT: As-Needed Decomposition and Planning with Language Models](https://mlsanity.com/paper/2311.05772/) (2023)
- [Monte Carlo Tree Search: A Review of Recent Modifications and Applications](https://mlsanity.com/paper/2103.04931/) (2021)
- [Making Large Language Models Better Planners with Reasoning-Decision Alignment](https://mlsanity.com/paper/2408.13890/) (2024)
- [Large Language Models Can Solve Real-World Planning Rigorously with Formal Verification Tools](https://mlsanity.com/paper/2404.11891/) (2024)
- [TravelAgent: An AI Assistant for Personalized Travel Planning](https://mlsanity.com/paper/2409.08069/) (2024)
- [Understanding the Capabilities of Large Language Models for Automated Planning](https://mlsanity.com/paper/2305.16151/) (2023)
- [Robust Planning with LLM-Modulo Framework: Case Study in Travel Planning](https://mlsanity.com/paper/2405.20625/) (2024)
- [Large Language Models as Common-Sense Heuristics](https://mlsanity.com/paper/2501.18816/) (2025)

## Built on by (most cited first, 21 of 21)

- [RLFactory: A Plug-and-Play Reinforcement Learning Post-Training Framework for LLM Multi-Turn Tool-Use](https://mlsanity.com/paper/2509.06980/) (2025)
- [DeepTravel: An End-to-End Agentic Reinforcement Learning Framework for Autonomous Travel Planning Agents](https://mlsanity.com/paper/2509.21842/) (2025)
- [Toward Personalized LLM-Powered Agents: Foundations, Evaluation, and Future Directions](https://mlsanity.com/paper/2602.22680/) (2026)
- [TP-RAG: Benchmarking Retrieval-Augmented Large Language Model Agents for Spatiotemporal-Aware Travel Planning](https://mlsanity.com/paper/2504.08694/) (2025)
- [TripTide: A Benchmark for Adaptive Travel Planning under Disruptions](https://mlsanity.com/paper/2510.21329/) (2025)
- [MobilityBench: A Benchmark for Evaluating Route-Planning Agents in Real-World Mobility Scenarios](https://mlsanity.com/paper/2602.22638/) (2026)
- [TravelEval: A Comprehensive Benchmarking Framework for Evaluating LLM-Powered Travel Planning Agents](https://mlsanity.com/paper/2606.01046/) (2026)
- [TREK: A Travel Reasoning and Evaluation Kit for LLM Agents in Complex Trip Planning](https://mlsanity.com/paper/2607.26977/) (2026)
- [TripScore: Aligning LLMs for Real-World Travel Planning via Expert-Calibrated Reward](https://mlsanity.com/paper/2510.09011/) (2025)
- [PlanningBench: Generating Scalable and Verifiable Planning Data for Evaluating and Training Large Language Models](https://mlsanity.com/paper/2605.20873/) (2026)
- [Replan, Repair, or Edit? A Unified Empirical Evaluation of Travel Agents for Itinerary Revision under Resource Disruptions](https://mlsanity.com/paper/2609.19654/) (2026)
- [VIR-Bench: Evaluating Geospatial and Temporal Understanding of MLLMs via Travel Video Itinerary Reconstruction](https://mlsanity.com/paper/2509.19002/) (2025)
- [Iti-Validator: A Guardrail Framework for Validating and Correcting LLM-Generated Itineraries](https://mlsanity.com/paper/2510.24719/) (2025)
- [MIND: Multi-agent inference for negotiation dialogue in travel planning](https://mlsanity.com/paper/2603.21696/) (2026)
- [Agentic AI for Trip Planning Optimization Application](https://mlsanity.com/paper/2605.00276/) (2026)
- [Revisiting the Travel Planning Capabilities of Large Language Models](https://mlsanity.com/paper/2605.03308/) (2026)
- [TourMart: A Parametric Audit Instrument for Commission Steering in LLM Travel Agents](https://mlsanity.com/paper/2605.10440/) (2026)
- [TransitLM: A Large-Scale Dataset and Benchmark for Map-Free Transit Route Generation](https://mlsanity.com/paper/2605.22355/) (2026)
- [MapSatisfyBench: Benchmarking Satisfaction-Aware Map Agents through Behavior-Grounded Implicit Decision Factors](https://mlsanity.com/paper/2606.17453/) (2026)
- [CARL: Constraint-Aware Reinforcement Learning for Planning with LLMs](https://mlsanity.com/paper/2607.04854/) (2026)
- [UTP-Bench: Uncertainty-aware Travel Planning Benchmark](https://mlsanity.com/paper/2609.02421/) (2026)

## Abstract (arXiv)

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.