Embark Now: User Demand Oriented Framework for Multi-day Urban Travel Itinerary Planning

2026-07-12 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Mobility Services & Technology · Depth: Expert, quick

Summary

The "Embark Now" framework addresses the complexities of multi-day urban travel itinerary planning, a challenge in large cities due to numerous Points of Interest (POIs), varied user preferences, and operational constraints like opening hours. This innovative framework integrates Large Language Models (LLMs) to precisely and flexibly capture dynamic user requirements, alongside an enhanced Greedy Randomized Adaptive Search Procedure (GRASP) algorithm for preference-aware itinerary generation. Extensive experiments on real-world datasets from Beijing and Tianjin demonstrate its effectiveness. The framework significantly outperforms leading existing methods, boosting the average total itinerary score by at least 4.52% and 11.09% across 5,040 user cases. Furthermore, it achieves average improvements of 17.95% and 26.07% in computed metrics, while also increasing time efficiency by 4.64% and 25.55% with shorter computation times.

Key takeaway

For AI Engineers developing urban travel or complex scheduling systems, "Embark Now" demonstrates a robust method to enhance solution quality and efficiency. If you are struggling with diverse user preferences and combinatorial constraints, you should consider integrating Large Language Models for dynamic requirement capture and an enhanced GRASP algorithm. This approach can yield substantial improvements, as shown by its 4.52% to 11.09% itinerary score increase and up to 26.07% efficiency gains over existing methods.

Key insights

Integrating LLMs for dynamic user preference capture with an enhanced GRASP algorithm optimizes complex multi-day travel planning.

Principles

• Dynamic user input is key for complex planning.
• Algorithmic enhancements improve quality and efficiency.
• Hybrid AI approaches outperform leading methods.

Method

The framework combines Large Language Models (LLMs) to dynamically capture user requirements with an enhanced Greedy Randomized Adaptive Search Procedure (GRASP) algorithm to generate feasible multi-day itineraries.

In practice

• Apply LLMs for nuanced preference extraction.
• Use GRASP for constrained combinatorial optimization.
• Test hybrid AI solutions on real-world datasets.

Topics

• Urban Travel Planning
• Itinerary Optimization
• Large Language Models
• GRASP Algorithm
• User Preferences
• Computational Efficiency

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.