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 Project in collaboration with Uber
Drivers were already talking to each other. We taught the platform to listen.
Designing a voice-first conversational AI layer for Uber drivers navigating large-scale events like FIFA World Cup 2026.
Field researchProduct designConversational AI
Team2 Product Designer (inc. me)
1 UX Researcher
1 Visual Designer
Uber Research & Design
DurationJan - Jun 2026
(6 Months)
Research through MVP
My ResponsibilityUX Design
Design Engineering
Conversational AI Integration
Stakeholder Management
ToolsFigma
Claude
Cursor
Eleven Labs
Live prototype
Try the working build below: live map, simulated event traffic, and a voice agent you can interrupt mid-sentence.
Voice may be offline if the ElevenLabs credits have run out. The teaser video shows the full spoken interaction.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. Uber runs at a scale where small gaps compound quickly, and 2026 brings the FIFA World Cup to the United States on top of it.
8.8 Million
Registered Uber drivers3.26 Billion
Trips completed in 2025396,000+
Large-scale events in US in first 90 days of 2026With 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.
95%prioritise real-time road information during large-scale events
60%avoid large-scale events altogether
THE GAP
Uber does validate driver road reports. The gap is latency, not absence. A report has to travel through Uber’s own validation channels before it reaches anyone else, and that round trip takes roughly two hours. At a large event the road changes in minutes, so by the time a closure is confirmed and pushed out to other drivers, it has usually already cleared or moved. The information is accurate and useless at the same time, which is why drivers fall back on each other instead.
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 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
PRIORITIZATION
Mapping Insights to Action
We mapped 8 research insights across driver impact & business effort to identify where design could move the needle most.
Trust
Communication
Reliability
Community
Transparency
The insights clustering in the high-impact quadrants all pointed to the same gap: real-time, trustworthy communication between the platform and drivers.
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
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.
The agent asks rather than guesses when speech is unclear.FEATURE 01
Conversational AI Bot
A voice-first assistant that carries road intelligence hands-free, so drivers keep their eyes on the road and their hands on the wheel. Every exchange is also written to the screen as a live transcript, and the agent runs on ElevenLabs, which supports 31 languages. A driver who is more comfortable in Punjabi or Spanish gets the same information as everyone else, and can read it back when crowd noise swallows the audio.
Design Rationale
Drivers told us they cannot look at screens during events, and many already coordinate by phone with other drivers. Voice removes the glance. Transcripts and language coverage are here because our interviews kept surfacing the same two failure points: engine and crowd noise drowning the audio, and drivers whose first language is not English missing information other drivers get for free.
Voice first, with a map pin as the fallback.FEATURE 02
Reporting a Closure
A driver who hits something the map does not know about can report it without touching the phone. The agent captures the location by voice, offers a map pin as a fallback when the street name is ambiguous, then asks what kind of obstruction it is: a road closure, a crowd disruption, an accident, or something else.
Design Rationale
Reporting had to survive a moving vehicle. Voice is the primary path and the pin is the backup, not the other way round. Typing was never an option: it is unsafe, and it is the reason so much of this intelligence currently lives in text threads between drivers instead of inside the app.
The prompt carries its evidence: five confirmations, verified five minutes ago.FEATURE 03
Validating Someone Else's Report
When another driver has already flagged something ahead, the agent surfaces it before this driver reaches it and asks for a yes or a no. The prompt carries its own evidence: how many drivers confirmed it, and how recently it was last verified. Either answer updates the report for everyone behind them.
Design Rationale
This is the direct answer to the two hour validation lag. Drivers are already the fastest sensor network on the road, so the confirmation loop runs driver to driver at the speed the road actually changes. Showing the count and the timestamp is what makes it trustworthy, because drivers told us they discount any alert that will not show its age.
05 / TESTING AND ITERATION
Four drivers used the prototype. Three things broke.
We ran moderated sessions on the mid-fidelity prototype and watched where a voice-first interface fails under conditions closer to real driving. Every finding below changed the design.
01
Cognitive Overload
The change
Three things to read became one question.
What drivers hit
The assistant introduced itself over two lines, then offered a pre-filled suggestion chip and a second button next to the mic. Drivers had to read and choose before they could speak.
What changed
Cut the introduction to a single question, removed the suggestion chip and the second button so the mic is the only control, and added an explicit dismiss.
02
Voice Focus
The change
The “Manual” button left the voice prompt.
What drivers hit
A full-width black “Manual” button sat directly under the voice prompt, competing with the mic. Two ways to answer the same question, and the manual one was visually louder.
What changed
Moved manual reporting out of the assistant and onto Uber’s existing report control on the map, leaving the assistant voice-only. Participants liked having the choice, so the path stayed, just not inside the prompt.
03
UX Copy
The change
The prompt now names the street it picked.
What drivers hit
“Select Where the Crowd Disruption Exists” asked drivers to confirm a road without showing which road the system meant. The whole route was highlighted, not the segment.
What changed
Rewrote it to “Set your pin on the street where the issue is located,” and made the map answer back: the chosen street is labelled and the exact stretch outlined, so drivers see the interpretation before confirming.
The change that mattered most
Drivers don’t speak in clean, short commands. They trail off, restate, and pause mid-sentence. We retuned the assistant to tolerate longer responses and natural pauses instead of cutting drivers off at the first silence.
What this does not prove
Four participants is enough for directional signal, not for a production decision. Validating this properly needs a larger and more diverse driver pool: varying tech comfort, languages, vehicle setups, and event types. It also needs a live event rather than a simulated one.
06 / TRY IT YOURSELF
The demo is real. Talk to it.
RoadRaise runs as a working web prototype: live map, simulated event traffic, and a voice agent you can interrupt mid-sentence. Built to test with drivers, not to sit in a deck.
ElevenLabs voice agentMapbox live reroutesNext.js on Vercel
05 / IMPACT BREAKDOWN
Mapping Insights to Action
PROJECTED USER IMPACT
Reduced pickup friction
Smart pickup zones & landmark wayfinding directly address the 3x longer pickup time during events, cutting wait times for both drivers and riders.
PROJECTED STRATEGIC IMPACT
Platform intelligence
By surfacing invisible driver knowledge into the platform, Uber gains a new data layer: crowd-sourced operational intelligence that improves with scale.
PROJECTED BUSINESS IMPACT
Driver retention
Large events are where drivers earn the most but also where frustration peaks. Better tools for event conditions directly impact driver satisfaction and retention.
06 / REFLECTION
The most surprising finding was that drivers had already built a better system than Uber offered, but they 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
- Start recruiting earlier. Many drivers were reluctant to talk about Uber on the record, so we had to get scrappy to fill the pipeline.
- Test designs in conditions closer to real large events. That is difficult to stage safely, and liability risk is high.
- Partner with Uber teams earlier for operational data: cancellation volumes, wait times, and related signals, so we could ground the work in clearer patterns.
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