Google’s latest innovation blends the creative strengths of large language models (LLMs) with the precision of optimization algorithms, revolutionizing how we approach travel itineraries. Google's latest search feature, "AI trip ideas in Search" suggests day-by-day itineraries.
A key challenge in launching this feature was ensuring the itineraries were practical and feasible. The solution uses a hybrid system, combining an LLM to suggest an initial plan with an algorithm that optimizes for similarity to the LLM plan and real-world factors like travel time and opening hours.
This approach integrates the LLM’s ability to handle soft requirements with the algorithmic precision needed for hard logistical constraints.
The Challenge: Personalization Versus Practicality
Everyone wants a customized trip, but translating those wishes into reality is tough. LLMs like Google’s Gemini are brilliant at interpreting qualitative requests; think “find cozy, kid-friendly restaurants” or “suggest scenic spots for sunset.”
However, these models can falter when juggling quantitative details such as opening hours, travel times, or budget constraints. This can lead to daydream-worthy plans that aren’t always doable, for example, scheduling a stop at a museum when it’s closed.
Google’s Hybrid Approach
To overcome these hurdles, Google designed a hybrid system for its AI trip planning feature in Search. The process begins with an LLM crafting a personalized itinerary based on user input.
Next, an optimization algorithm steps in, rigorously checking and adjusting the plan to account for real-world factors: updated business hours, travel distances, and even substitute activities if something’s unavailable. This ensures the final plan reflects your interests and is actually possible to execute.
Behind the Scenes: The Optimization Process
- Stage 1: Daily Scheduling – The system examines all potential activity combinations for each day. It scores these based on how closely they match the LLM’s initial suggestions and whether they’re feasible, taking into account opening hours and travel duration. Techniques like exhaustive search and dynamic programming help evaluate every option.
- Stage 2: Itinerary Refinement – The algorithm then pieces together a multi-day itinerary, maximizing overall quality without activity overlap. This complex process (similar to solving a weighted set packing problem) uses local search heuristics, making incremental tweaks until the plan is both optimized and executable.
Real-World Impact: Smarter Itineraries
Google’s approach shines in practical scenarios. For instance, a user asking for under-the-radar museums in New York City to avoid crowds received a tailored, crowd-free itinerary. In contrast, a traditional search-only method recommended popular, busy venues, missing the user’s intent.
Another example in San Francisco showed the optimizer improving an LLM-proposed schedule by grouping nearby activities, cutting down on unnecessary travel time.
Query: “Plan me a trip to San Francisco. I want to visit art museums and go somewhere with panoramic views of the city.”
During testing, the LLM suggested a number of good attractions, such as the de Young museum (which also includes an observation tower) and the iconic Coit Tower.
However, the itinerary scheduled the activities for one of the days in an unnatural way, requiring the user to travel across the city. They were able to correct this in the optimization step, resulting in a logistically feasible plan that preserves the original intent.
The LLM-suggested itinerary (left) includes a long travel across the city on one of the days. After applying an optimization algorithm, we obtain a more natural grouping of activities (right).
Beyond Travel: The Future of AI-Assisted Planning
This research suggests a broader future for LLM-powered planning: extending to event organization, errand scheduling, and more. The magic lies in merging AI’s intuitive grasp of preferences with robust systems that handle real-world details. Google continues to refine this blend, aiming to bring smarter, more reliable AI support to everyday life.
By marrying LLM creativity with the discipline of optimization algorithms, Google’s hybrid trip planning system demonstrates how AI tools can move beyond idealized plans to deliver real, actionable solutions tailored to each user. The future of AI-assisted planning looks both bright and practical.
Source: Google Research Blog
Google’s AI Balances Creativity and Logistics in Trip Planning