Off-Platform Communication
Drivers share real-time road intelligence through informal channels, text chains, group chats, voice calls, that Uber cannot see or act on.
8 of 12 drivers used off-platform networks
Capstone · UW HCDE × Uber · 2026
Reimagining the driver experience when the city turns into a stadium.
Research, synthesis, and conversational AI design for FIFA World Cup 2026 and other surge moments when reliability and trust matter most.
Field researchProduct designNative surfacesConversational AILarge-scale events
01 / THE PROBLEM
Uber is designed for normal conditions. When 50,000 fans leave a stadium at once, the platform breaks down, and drivers are left to adapt on their own.
"Sometimes the Uber app picks it up and sometimes it doesn't. I don't know how well they coordinate with the police shutting down streets, more times than not, they don't."
P1, Uber Driver, Seattle
This matters now. The 2026 FIFA World Cup is coming to the United States, and the scale is hard to overstate.
5.5 Million
Fans projected to attend in US, 2026396,000+
Large-scale events in US in first 90 days of 20263x
Avg. pickup time increase during events vs. normalWith millions of fans unfamiliar with host cities, Uber has a significant opportunity to become the default way to get around. But only if the driver experience holds up under pressure.
Event-Day Driver Journey
PRE-TRIPChecking events, setting destination filters
FINDING SPOTSDriving to venue, hunting for staging
WAITINGStaging at personal spot, waiting for surge
TRIP REQUESTPEAKAccept or decline in ~10 seconds
MATCHPEAKNavigate to customer, coordinate pickup
IN-ROUTERoad closures, GPS errors ahead
EXITBlocked routes, police redirects
02 / HOW WE LOOKED
We didn't start with a survey. We started where drivers actually work.
Uber
Platform goals, business priorities, internal data
Earners (Drivers)
On-the-ground experience, pain points, workarounds
Event Organizers
Venue logistics, crowd flow, road closures
Public Transit + City Systems
How other systems handle surge, coordination gaps
Our research centered drivers, but understanding the full ecosystem shaped how we framed the problem and where we drew design boundaries.
RIDE-ALONG OBSERVATIONS
Accompanied drivers on event-day trips in Seattle. Observed navigation decisions, passenger interactions, and staging strategies in real time.
4 sessions | 16+ hours
IN-DEPTH DRIVER INTERVIEWS
Remote and in-person interviews across three cities exploring mental models, coping strategies, and event-day pain points.
12 participants | 45-60 min each
STAGING AREA WALKTHROUGHS
Visited known staging and pickup spots near venues. Documented spatial patterns and informal driver coordination.
3 venues | Photos + notes
DRIVER COMMUNICATION LOGS
Analyzed screenshots from driver group chats, forum posts, and personal note systems, the invisible knowledge networks.
200+ messages reviewed
SECONDARY RESEARCH
Building on What Was Already Known
Before speaking with a single driver, we reviewed Uber driver app store reviews, Reddit communities (r/uberdrivers), competitor pickup flows across 7 platforms (Lyft, Waymo, Lime, Shuttle, Gett, Curb), and 5 academic and industry sources on large-event transportation logistics. This grounded our interview protocol in real patterns, not assumptions.
03 / WHAT WE FOUND
We expected frustration with navigation. Instead, we found drivers had already solved those problems, through an invisible strategy layer the platform never sees.
Local Positioning Strategies
Veteran drivers develop mental maps of secret staging locations, optimal pickup routes, and positioning strategies built over years of experience.
100% of veteran drivers had spot strategies
Pickup Point Friction
The pickup moment during events is the highest-friction point. Drivers and riders struggle with location accuracy, crowd density, and unclear meeting points.
Avg. 3x longer pickup during events vs. normal
"The app tells you to go to the designated zone. But you'd be stuck there for 20 minutes. Experienced drivers know to wait 2 blocks over."
P4, Veteran Uber Driver
04 / THE DESIGN RESPONSE
How might we surface real-time, trustworthy guidance so drivers can make smarter decisions without relying on informal workarounds?
Solutions Considered
CHOSEN
Real-Time Info via Conversational AI
Goals
Reach pickup more efficiently, offload misinformation on reroutes
Value
Hands-free comms, crowdsourced reroutes, reduces info overload for drivers and Uber
Why chosen
Directly addresses Ghost Frequencies and Match Point Stress. Voice-first removes the 'glance at screen' constraint. Can scale to non-English speakers.
Haptic Radius Feedback
Goals
Find passenger efficiently, reduce back-and-forth communication
Value
Hands-free proximity alerts, customizable radius
Why not chosen
Solves a narrower slice of the problem. Haptic hardware variation across devices creates reliability risk.
Guess-timation and Data Viz
Goals
Data-informed decisions about when to make trips, demand transparency
Value
Increased earnings through demand forecasting, density maps
Why not chosen
Valuable but addresses pre-trip planning, not the in-event communication breakdown.
THE SOLUTION: ROADRAISE
A conversational AI layer inside the Uber Driver app designed for hands-free, real-time communication during large-scale events.
FEATURE 01
Conversational AI Bot
A voice-activated assistant that communicates road closures and reroutes hands-free. Drivers interact through speech, keeping eyes on the road and hands on the wheel.
Design Rationale
Drivers told us they can't look at screens during events. Ghost Frequencies showed they already call each other for this information. We formalized that behavior into the platform.
[PROTOTYPE: Conversational AI Bot, voice active state]Hi-fidelity prototype in progress
FEATURE 02
Validation and Reporting
Drivers can crowdsource real-time roadblock reports through the voice interface. Reports are validated by the platform and surfaced to other drivers in the area.
Design Rationale
Drivers have been offloading Uber's update burden for years through Facebook groups. This brings that behavior into the app with quality controls and trust signals.
[PROTOTYPE: Validation flow, report submitted state]Hi-fidelity prototype in progress
FEATURE 03
Transcripts and Translation
Every voice interaction generates a readable transcript. Multilingual support ensures drivers who don't speak English natively can access the same information.
Design Rationale
Match Point Stress research surfaced that language barriers between drivers and riders (and drivers and the platform) compounded pickup friction. Transcripts give drivers a fallback they can verify.
[PROTOTYPE: Transcript UI, multilingual view]Hi-fidelity prototype in progress
05 / REFLECTION
The most surprising finding was that drivers had already built a better system than Uber had, they just built it outside the app. The design challenge wasn't to invent new behavior. It was to earn enough trust to bring existing behavior onto the platform.
What Worked
- Field studies over surveys. Ride-alongs showed us things no interview would have surfaced.
- Reframing the problem. Treating this as a communication design challenge, not a navigation problem, opened up the solution space.
- Co-design with drivers. Validating three directions against driver willingness revealed adoption blockers early.
What We'd Do Differently
- Lock roadmap dependencies earlier with maps and venue ops — we'd clarify which closure, zone, and demand signals product can commit to displaying before proposals outpace what engineering can responsibly ship.
- Second-city validation sooner — we'd run a lighter duplicate of Seattle's field playbook in another high-volume event market earlier, so generalized claims aren't riding on one metro's texture.
- Multilingual recruiting from sprint zero — we'd screen and moderate in drivers' dominant languages concurrently with English ride-alongs, instead of patching translation gaps once concepts were frozen.
RoadRaise is in active design refinement ahead of driver concept testing.
Selected Projects
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Conducted field research to understand painpoints and make driver experience more reliable and trustworthy during large-scale events so Uber can capture more trips when demand is highest.