# ITINERA: Integrating Spatial Optimization with Large Language Models for Open-domain Urban Itinerary Planning

arXiv: https://arxiv.org/abs/2402.07204 · HTML: https://mlsanity.com/paper/2402.07204/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 11 Feb 2024.

## Builds on (most cited first, 12 of 12)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [TravelPlanner: A Benchmark for Real-World Planning with Language Agents](https://mlsanity.com/paper/2402.01622/) (2024)
- [ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation](https://mlsanity.com/paper/2107.02137/) (2021)
- [Where Would I Go Next? Large Language Models as Human Mobility Predictors](https://mlsanity.com/paper/2308.15197/) (2023)
- [Video Background Music Generation with Controllable Music Transformer](https://mlsanity.com/paper/2111.08380/) (2021)
- [Leveraging Language Foundation Models for Human Mobility Forecasting](https://mlsanity.com/paper/2209.05479/) (2022)
- [Video Background Music Generation: Dataset, Method and Evaluation](https://mlsanity.com/paper/2211.11248/) (2022)
- [TrafficSafetyGPT: Tuning a Pre-trained Large Language Model to a Domain-Specific Expert in Transportation Safety](https://mlsanity.com/paper/2307.15311/) (2023)
- [Large Language Models for Travel Behavior Prediction](https://mlsanity.com/paper/2312.00819/) (2023)
- [POIBERT: A Transformer-based Model for the Tour Recommendation Problem](https://mlsanity.com/paper/2212.13900/) (2022)
- [Large language model empowered participatory urban planning](https://mlsanity.com/paper/2402.01698/) (2024)

## Built on by (most cited first, 30 of 33)

- [A Survey on Personalized Alignment -- The Missing Piece for Large Language Models in Real-World Applications](https://mlsanity.com/paper/2503.17003/) (2025)
- [Large Language Models for Planning: A Comprehensive and Systematic Survey](https://mlsanity.com/paper/2505.19683/) (2025)
- [Sparkle: Mastering Basic Spatial Capabilities in Vision Language Models Elicits Generalization to Spatial Reasoning](https://mlsanity.com/paper/2410.16162/) (2024)
- [Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmap](https://mlsanity.com/paper/2503.21411/) (2025)
- [Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey](https://mlsanity.com/paper/2503.13502/) (2025)
- [Toward LLM-Agent-Based Modeling of Transportation Systems: A Conceptual Framework](https://mlsanity.com/paper/2412.06681/) (2024)
- [Large language models for artificial general intelligence (AGI): A survey of foundational principles and approaches](https://mlsanity.com/paper/2501.03151/) (2025)
- [DeepTravel: An End-to-End Agentic Reinforcement Learning Framework for Autonomous Travel Planning Agents](https://mlsanity.com/paper/2509.21842/) (2025)
- [TP-RAG: Benchmarking Retrieval-Augmented Large Language Model Agents for Spatiotemporal-Aware Travel Planning](https://mlsanity.com/paper/2504.08694/) (2025)
- [RETAIL: Towards Real-world Travel Planning for Large Language Models](https://mlsanity.com/paper/2508.15335/) (2025)
- [Large Language Models and Their Applications in Roadway Safety and Mobility Enhancement: A Comprehensive Review](https://mlsanity.com/paper/2506.06301/) (2025)
- [Vision-to-Music Generation: A Survey](https://mlsanity.com/paper/2503.21254/) (2025)
- [Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications](https://mlsanity.com/paper/2507.00914/) (2025)
- [WorldTravel: A Realistic Multimodal Travel-Planning Benchmark with Tightly Coupled Constraints](https://mlsanity.com/paper/2602.08367/) (2026)
- [A Short Survey of Human Mobility Prediction in Epidemic Modeling from Transformers to LLMs](https://mlsanity.com/paper/2404.16921/) (2024)
- [INTENT: Trajectory Prediction Framework with Intention-Guided Contrastive Clustering](https://mlsanity.com/paper/2503.04952/) (2025)
- [Urban Computing in the Era of Large Language Models](https://mlsanity.com/paper/2504.02009/) (2025)
- [E3AD: An Emotion-Aware Vision-Language-Action Model for Human-Centric End-to-End Autonomous Driving](https://mlsanity.com/paper/2512.04733/) (2025)
- [TravelEval: A Comprehensive Benchmarking Framework for Evaluating LLM-Powered Travel Planning Agents](https://mlsanity.com/paper/2606.01046/) (2026)
- [LLMSynthor: Macro-Aligned Micro-Records Synthesis with Large Language Models](https://mlsanity.com/paper/2505.14752/) (2025)
- [TripScore: Aligning LLMs for Real-World Travel Planning via Expert-Calibrated Reward](https://mlsanity.com/paper/2510.09011/) (2025)
- [TRACE: Tourism Recommendation with Accountable Citation Evidence](https://mlsanity.com/paper/2605.07677/) (2026)
- [Think2Go: Generative Next POI Recommendation with LLM Reasoning](https://mlsanity.com/paper/2607.28997/) (2026)
- [RelationalFactQA: A Benchmark for Evaluating Tabular Fact Retrieval from Large Language Models](https://mlsanity.com/paper/2505.21409/) (2025)
- [From Street Views to Urban Science: Discovering Road Safety Factors with Multimodal Large Language Models](https://mlsanity.com/paper/2506.02242/) (2025)
- [Iti-Validator: A Guardrail Framework for Validating and Correcting LLM-Generated Itineraries](https://mlsanity.com/paper/2510.24719/) (2025)
- [Agentic AI for Trip Planning Optimization Application](https://mlsanity.com/paper/2605.00276/) (2026)
- [MobEvolve: An Agentic Self-Evolving Heuristic System for Interpretable Human Mobility Generation](https://mlsanity.com/paper/2606.01640/) (2026)
- [Embark Now: User Demand Oriented Framework for Multi-day Urban Travel Itinerary Planning](https://mlsanity.com/paper/2607.10651/) (2026)
- [TSCA-Net: Temporal-Spatial Clique Attention for Interpretable Multimodal Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2607.11939/) (2026)

## Abstract (arXiv)

Citywalk, a recently popular form of urban travel, requires genuine personalization and understanding of fine-grained requests compared to traditional itinerary planning. In this paper, we introduce the novel task of Open-domain Urban Itinerary Planning (OUIP), which generates personalized urban itineraries from user requests in natural language. We then present ITINERA, an OUIP system that integrates spatial optimization with large language models to provide customized urban itineraries based on user needs. This involves decomposing user requests, selecting candidate points of interest (POIs), ordering the POIs based on cluster-aware spatial optimization, and generating the itinerary. Experiments on real-world datasets and the performance of the deployed system demonstrate our system's capacity to deliver personalized and spatially coherent itineraries compared to current solutions. Source codes of ITINERA are available at https://github.com/YihongT/ITINERA.