ParkMobile
Reducing driver frustrations with parking by improving clarity and planning in ParkMobile
TYPE
Sponsored
TIMELINE
16 Weeks
TEAM
Self
3 Students
ParkMobile UXR
ROLE
Led design, assisted with UXR
Overview
A sponsored 16-week project with ParkMobile and its parent company, Arrive, exploring how to boost early engagement in the ParkMobile app and ease driver frustrations with paid parking. I worked with three other students, with ParkMobile’s UX Research lead as our point of contact.
Setting the Scene
the urban parking problem
How does finding parking make you feel?
If you said frustrating, infuriating, or a similar adjective, you're not alone in that sentiment. We found that:
82%
of drivers identify parking as their top urban stressor (ParkMobile 2023)
75%
of drivers say poor parking infrastructure discourages them from going out (ParkMobile, 2024)
parkmobile
Enter ParkMobile.
ParkMobile is North America's leading parking platform with over 65+ million users across 700+ cities. ParkMobile addresses the urban parking problem through it's two core features - Parking Search & Reservation and Contactless Parking Payment.
Parking Search + Reservation
Contactless Parking Payment
the problem
However, ParkMobile came to us with a problem:
Most ParkMobile users only engage at the payment stage - revealing a gap between features and usage.
project goals
In order to address this problem, we set out two goals for ourselves in this project, and asking ourselves the following questions:
the answer
We ultimately answered these goals with two design proposals:
Enhancing pre-parking planning
By enhancing pre-parking planning, Drivers can search their destination before leaving to preview nearby parking zones, comparing price, amenities, and availability so they know where to park before reaching busy areas.
Providing drivers with parking assistance
Our solution introduces an AI Parking Assistant that when prompted, recommends locations based on availability, pricing, distance, and other preferred factors.
Research
research methodology
Focusing on urban drivers, we used a holistic research approach to learn more about driver goals and frustrations, using quantitative and qualitative approaches.
User Interviews
14 interviewees
Surveys
70 responses
Task Analysis
ParkMobile App
Competitive Analysis
Parking App Competitors
Secondary Review
9 sources
During our research process, I had the opportunity to lead a number of our user interviews, distributed our survey, and conducted the competitive analysis
research findings
Our research led us to these four findings ->
01
Price matters most
Cost is one of the biggest factors with parking
02
Context is key
Drivers rely on parking context like type, distance, and amenities
03
Time of day matters
Situational factors like time of day impact parking decisions
04
Planning starts early
Drivers are already thinking about parking before driving
existing issues
01
Lack of filtering
Parking selection lacks filtering for price, lot type, safety, or amenities
02
Unclear parking availability
Zone maps don’t communicate where parking is actually available
03
No context around parking
The app offers little to no context about a zone before drivers commit
journey map
In order to visualize the driving journey, I created this journey map. As you can see, a lack of parking knowledge before the drive, ultimately leads to frustrations while parking.
design requirements
Each finding was then translated into a set of user needs. Based upon the user needs, we created design requirements for each finding. These requirements would help guide our next phase in the project with design iteration.
Price clarity
Show pricing on every spot, with price filters
Contextual awareness
Surface parking details like ADA and EV, with preference filters
Time-based planning
Visualize parking availability across the map by time of day
Pre-trip planning
Eliminate parking guesswork with answers before the drive
Design Iteration
iteration process
Our design process went through four phases: initial sketches, concept selection, wireframing, and testing plus polishing. Individually, the team came up with different sketch ideas that best met our laid out design requirements. The group then held a critique to select which concepts to move forward with. From there, the team wireframed, tested with users, and continued to polish designs.
01
Initial Sketches
02
Concept Selection
03
Wireframes
04
Testing + Polishing
pre-parking planning
Exploring ideas around a parking heat map
I led design of the parking availability indicator, exploring a heat map to show parking across an area and a spider chart to compare locations at a glance.
Heatmap + spider chart design explorations
I used Claude Code to generate variations of parking availability indicators. It produced some unusual options, like a hex grid and a dot map, but I settled on the heat map.
Iterating on Feedback
I tested spider chart and heat map designs with teammates and users. The spider chart fell flat: users couldn’t make sense of it in the first few seconds and wanted something simpler. So I moved to a card design that surfaces key attributes like price, location, and amenities.
parking search + filtering
Design Explorations
I also led the parking search and filtering design. Using Claude Code again, I explored input options for the filters. I moved forward with the highlighted designs, which best fit key attributes like price and amenities.
Iterating on Feedback
I added the highlighted designs to the filters page, then used feedback from teammates and users to match each input design to the right parking attributes.
parking assistance
AI Parking Assistant
I also designed the AI Parking Assistant, a chatbot drivers can talk to by text or voice.
Parking Detail Pages
I helped design the parking detail pages, which show reviews, safety information, and, for garages, live availability.
Iterating on Feedback
Based on user feedback, I refined the wireframes, adjusting hierarchy, color, and text.
Evaluations
usability findings
Through further usability studies, we found lingering issues around visual interpretation and friction.
Parking availability is still getting misread
In testing, users kept misreading the heatmap: they saw red as “no parking,” when it actually marked where spots were open. I simplified the map again, replacing the heatmap with individual parking zones color-coded by availability.
Search filters were hard to reach
I found that users were frustrated with having to go back to the search bar in order to change parking filters. To solve this, I surfaced parking filters to the map itself, allowing users to edit filters without having to go back into search.
Telling, not showing with parking intelligence
I realized the AI Parking Assistant was telling, not showing. Users wanted visual, actionable answers, so I moved from a basic chat interface to one that shows parking recommendations right on the map.
The Solution
the solution
Parking Search & Filtering
Search and filter for parking based on a number of preferences such as price, distance to destination, and amenities.
Parking Map
See live parking availability near you, plus how far each spot is from where you’re headed.
AI Parking Assistant
Get parking answers and recommendations from an AI assistant, by chat or voice.
Parking Details & Garage Availability
Get reviews, safety info, amenities, and accessibility details for every spot, and live space counts for garages.
Reflection
validation
We found that the redesign help up in testing.
We were able to complete hallway usability tests with 8 participants.
8/8 participants found key parking info
Labeled zones fixed the availability misinterpretation
8/8 used parking filters successfully
Surfacing filters to the map removed friction around access
6/8 participants found parking intelligence helpful
Drivers valued actionable answers, but 2/8 wanted greater integration throughout the app
takeaways
We were able to present our project to designers on the ParkMobile team and other stakeholders. Along with the valuable feedback, there were a couple takeaways I had.
Show ideas the door when needed
My explorations around the parking heat map showed me not to overcomplicate, and not to fall in love with ideas.
AI for breadth, users for depth
I learned to use AI tooling for wide exploration, but found that the real issues were found with users.
