Context
Long journeys require breaks, yet most drivers still make these decisions by default, not by choice.
Today, rest stops are discovered reactively, through road signs, GPS suggestions, or habitual routes and rarely with confidence about what’s ahead. Existing navigation tools prioritize efficiency over experience, leaving drivers unsure whether an upcoming area meets their needs (rest, food, cleanliness, price, safety, etc.).
R started from a simple observation: while planning a long drive, I realized that there was no intuitive way to anticipate the quality of rest stops.
This project explores how clarity, anticipation, and personalization could help users plan their breaks rather than endure them.
Problem Space
Early research revealed a consistent insight:
“Stops are not chosen, they’re endured.”
To explore this further, I conducted both quantitative and qualitative research:
Survey (50+ respondents) to understand travel frequency, motivations, and pain points.
In-depth interviews with drivers and families to identify behavior patterns and emotional triggers.
Journey mapping and empathy maps to visualize experiences before, during, and after a stop.
Key findings:
Stops are often unplanned, triggered by fatigue, hunger, or children’s needs.
Drivers rely on road signs and timing, not on useful or reliable data.
Existing apps provide limited, outdated, or irrelevant information (e.g. missing details about pricing, crowding, or service quality).
Two main driver profiles emerged:
The Practical: wants a quick, functional stop to save time.
The Selective: ready to drive longer for a better experience (cleaner, quieter, or greener area).
These insights shaped the main challenge:
How might we help drivers anticipate their next stop and make better, more confident decisions without adding friction to their journey?
Solution Space
1. Definition
Insights were structured into opportunity areas and “How Might We” statements, such as:
How might we help drivers anticipate breaks without losing time?
How might we make stops more aligned with personal needs (rest, food, work, family)?
How might we provide reliable, up-to-date information about each rest area?
2. User Flow
From these questions, I defined a clear, minimal journey:
Enter a trip → input start and destination, or sync directly with Waze/Google Maps.
View stops ahead → displayed in list or map view.
Filter results → by services (restaurants, WiFi, green spaces, fuel price).
Select a stop → detailed page showing photos, ratings, and real-time info (price, crowd level, etc.).
Launch navigation → redirect to preferred GPS app for the chosen stop.
The interface was designed around clarity and focus: limited cognitive load, actionable data, and zero friction between decision and action.
3. Edge Cases
No data / connectivity → fallback to cached results with basic info.
No matching results → display alternative recommendations or larger search radius.
Outdated info → visible disclaimers with last update timestamps.
Quick rerouting → allow re-sync if trip direction changes mid-journey.
The interface has now reached a first stable UI version, following several iterations on layout and interaction logic. A qualitative testing phase is underway on Maze, focused on validating navigation clarity, perceived usefulness, and overall comprehension of the experience.
Next steps will include refining micro-interactions and accessibility details based on insights from user feedback, before moving toward a pre-MVP build.
All Works
