Smart Travel Planning
with AI-Powered Route Tracking




Problem
Trip details are scattered across different channels, making coordination chaotic and stressful.
Solution
Developed an AI-powered travel app that parses travel links, auto-generates itineraries, and provides interactive map navigation, helping users plan trips faster and more efficiently.
Impact
Role
Product Designer
Contribution
UX design
Feature Scoping
Accessibility Design
A/B Testing
Timeline
Jan 2025 - Present
Team
2 UX designer
1 UX reseracher
1 Product Manager
3 Software Developers
Core Experiences
Triple is a intelligent travel-planning App featuring smart link parsing, automated itinerary generation, and interactive map-guided navigation to reduce trip planning time and increase efficency.

Smart link recognition
Effortlessly import travel spots from popular social media and save them with a single tap.
Travel map + Itinerary
Location · Transportation · Customizable Trip Types


AI Realtime travel suggestion
AI provides flexible, real-time itinerary suggestions based on weather.
Problem
Trip details are scattered across multiple channels without a unified sync, causing chaotic coordination and high stress。
User research
Explore Problem Space
100+
Articles
20+
Interviews
350+
Surveys
OUR USERS
Understanding Users
UNDERSTANDING USERS
Dynamic User Journey Map
Design Process
1.Layout
2.Intercation Design
3.Design Validation
ITERATION
Iteration towards Layout
Information Architecture
How to absorb content on Place Detail Page effectively?
A/B Testing on Layout
Balancing information density vs. clarity in mobile interface
Result
33% more content per page!
33%
INTERACTION ITERATION
Iteration towards Interaction

Solution 1:
Prioritize displaying the map, with the itinerary fixed in a dedicated window

Solution 2:
Prioritize displaying the priority
ITERATION
Even with thorough planning, unexpected disruptions can ruin expectations and lead to frustration.
Opportunity 1
AI Travel Tips
I designed the AI-generated Travel Tips module to proactively surface context-aware recommendations, combining:
Location-specific advice(e.g., NY tap water is cold, dry air in March)
Seasonal considerations(e.g., layering in winter)
User intent recognition(e.g., camera gear for content creators)
Packing psychology(essentials vs bonus)
Instead of giving generic tips, we deliver personalized and purposeful suggestions that align with user mindset: “What will I actually need there?”


Opportunity 2
Adaptive Planning: Weather-Aware Itinerary Updates
AI + crowd insights (“80% of users recommend this”) builds trust
Minimal interruption via one-screen, two-option UI
Flexible control — no forced changes, user decides
Map-based visualization grounds the recommendation in real context

Impact
Design Validation
Iteration towards Feasibility
Evaluating concepts
Usability tests
Users showed a clear preference for the time-segmented layout, which better supports mental organization, task anticipation, and real-time decision making.
Technical constraint: Balance performance & cost constraints
Infinite scroll caused performance issues and high backend costs due to frequent API calls under real-time data.
We replaced it with a lightweight paginated view to improve stability and reduce server load.
Design Validation