PLANR: A Public Transit Planning App

  • Time: 6 month bootcamp project

  • Problem: How can we improve the digital experiences involved in riding and navigating public transit?

  • Target users: People who ride public transportation at least once a week or more

  • Scope and constraints: Limited by time - create an MVP and identify future feature “releases”

  • Outcome: On final version of MVP prototype, users had a 96% success rate (tested via Maze)

Planr: A Public Transit Planning App

I led this 6-month Springboard (bootcamp) project as the “UX Lead,” creating PLANR, a prototype iOS app that helps users easily plan and save public transit journeys, stops, and stations.

Click here to view the full prototype 📱 (This will open in a new tab.)

Phase 1: Empathize (Research)

Secondary Research

Initially my plan was to focus on the mobile payments experience for DC public transit, so I first reviewed similar apps in other cities like Portland and NYC. What were the basic features of these apps, and how did they look & feel? What were app store reviews saying? Click here to see my secondary research presentation.

User Interviews

But to get some real “user feedback,” I interviewed 7 participants, 3 DC residents and 4 Springboard students, all of whom regularly used public transit. I even threw in a $15 Amazon gift card for people who participated :)

I found people to interview by passing out flyers at my local metro stop during rush hour.

Phase 2: Define (Synthesize)

After these interviews, I synthesized over 3.5 hours of interview recordings, and 18 pages of notes into an affinity map.

From these conversations, I realized mobile payments weren’t the issue at all - most users I spoke to had already switched to digital wallets, which the DC metro card supports. The real issue had to do with trip planning!

My affinity mapping exercise identified the following themes:

  • Planning vs Mapping: Frequent transit users already knew how to get where they’re going (mapping) - what they needed to know was the best time to leave (planning)

  • Need for Real-time Updates: Nearly every user stressed the importance of accurate, real-time arrival and departure information.

  • Multi-modal Transit: Many riders used more than one form of transportation, like switching between bus and metro, but existing apps didn’t handle this well.

From the 7 transit riders I interviewed, I also created 3 personas to guide what the average user types for this “app” might be. These personas showcase the need to design for frequent high-stakes riders, frequent low-stakes riders, and suburban commuters.

Phase 3: Ideate + Refine

These insights informed three "How Might We" (HMW) questions that guided my design:

  1. HMW plan efficient public transit journeys?

  2. HMW provide real-time updates for transit users?

  3. HMW support multi-modal transit trips?

I brainstormed solutions to my HMW questions using the Now, Wow, How method, narrowing down features to focus on what was essential.

The four core features, or “red routes,” were:

  1. Start a new journey

  2. View saved journeys

  3. Save stops or stations

  4. Check arrivals at saved locations

I sketched initial ideas and refined them based on feedback from my mentor.

Phase 4: Lo- to Hi-Fidelity

I developed wireframes in Figma, focusing on minimizing clicks for users who are often on the go. Based on testing feedback, I ensured that users could:

  • See all saved journeys right from the home page.

  • Access saved stops and stations with fewer clicks.

In the hi-fidelity phase, I created a style guide with a vibrant blue palette and a sleek, modern UI. I also streamlined navigation by eliminating a lower nav bar, opting for an overlay with tabs for Saved Journeys and Stations. My goal was to simplify and modernize the user experience, reducing friction at key touchpoints.

Phase 5: Test + Iterate

I conducted two rounds of usability testing. In the first round, I moderated tests via Zoom with 5 users. Key insights:

  • Task success: Users easily completed tasks like planning a trip and saving a station.

  • Navigation tweaks: Based on feedback, I reduced the number of clicks for accessing saved journeys and stops.

In the second round, 26 users completed unmoderated testing via Maze.co. Tasks had a 96% success rate, and feedback was overwhelmingly positive. However, due to time constraints, I was unable to incorporate feedback from the second round.

Conclusion + Next Steps

If I had more time, I’d prioritize developing an in-transit experience—features like trip notifications and dynamic directions that update during the journey.

I’d also revisit ideas from the ideation phase, such as scheduled journeys and third-party integrations (e.g., Uber, Lyft, bikeshare).

Overall, this was my first time designing an end-to-end digital experience, so this project will always have a special place in my heart!

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