In this pilot project I used the design process to tackle the brief: "Increase the number of drivers on shift for a particular rideshare company to meet the demands of customers in San Francisco, particularly at peak times."

1. Tyler Tate

2. Increase the number of drivers on shift to meet the
demands of customers in San Francisco, particularly
at peak times.
The brief

3. t
User research
Problem definition
Ideation
Prototyping
Testing
My approach

4. User Research

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I’ve never driven for a rideshare company, so I feel
far removed from the problem. To help me
empathize with drivers, I took a couple of rides—
one on Uber, the other on Lyft—and asked the
drivers questions about how they choose where
and when to drive. Here’s what I learned.
William works Uber full time. He aims to maximize
his number of rides and minimize his down time to
achieve his target income of $200/day. He is
skeptical of chasing surge fares, citing the delays of
bad traffic often associated with surge events, and
instead stays focused on doing as many rides as
possible. He often seeks out places like shopping
malls and the Stanford campus that’s he found have
short waits between passengers.
William, pro Uber driver

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Abay just started driving for Lyft last week, aiming
to earn money in his spare time. He’s still trying to
get the hang of things and hasn’t developed a
strategy for choosing where to drive—he just starts
from home (South San Jose) and sees where his
passengers take him. He’s tried going into
downtown San Jose and heading towards “prime
time” areas (Lyft’s version of surge), but it hasn’t
paid off for him yet; he’s gone to a surge area a
couple of time only to pickup a non-surge fare in
the end (this is a common complaint among Lyft
drivers in online forums).
Abay, new Lyft driver

7. Problem Definition

8. t
Neither driver I talked to found it effective to pursue
surge fairs, and drivers in online forums dismiss
“chasing the surge” as an ineffective strategy.
This is a problem for rideshare companies. It
indicates that surge pricing is not effective as a
mechanism for balancing supply and demand.
My research suggests the reason drivers ignore the
surge signal is because negative factors—such as
bad traffic and lengthy pickup times—often
counteract the benefit of higher fares.
To get drivers to respond to increased demand,
therefore, we need to provide them with both the
positive and negative info they need to make
informed decisions about maximizing their income.
The problem

9. Ideation

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Alerts
Earnings / Time
One approach
would be to alert
based on projected
earnings over time.
Multiple Alerts
Another approach
would be to support
multiple highly-
configurable alerts.
Alert Config
Thresholds not just
for surge, but also
fares, ETAs, waits,
and proximity.
Alerting drivers when they can
earn more money than usual
would help get more drivers
on the road at peak times.
The existing Uber app does
provide surge notifications.
However, surge is only one of
many factors that determines a
driver’s earning potential.
Here are two ideas for more
driver-centric notifications: 1)
“Alert me when I can earn $X
in Y hours”, and 2) "Alert me
when X, Y, and Z occur within
N miles of me.”

11. t
Plan
Where to go?
Select the number of
hours you’d like to
work, then see
earning projections.
Area detail
Tap on an area for
details, including a
histogram showing
projected earnings.
When to go?
Manipulate the
histogram to see how
much you could earn
at different times.
“Where should I go to
maximize my earnings for the
day?” Helping drivers answer
that question accurately would
both optimize driver earnings
as well as Uber’s supply-side.
One way to facilitate that could
be to introduce a “Plan” tab.
Using a slider, the driver could
input how many hours they’d
like to work. The map could
display median projected
earnings for that time period.
Tapping on a given area would
provide additional details,
including a 24-hour histogram.

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Map overlays
Overlay button
Like Google Maps, a
“layers” icon could
be used to reveal a
map options menu.
Menu sheet
Select what type of
information you’d
like to see on the
map.
Overlay
Want to see where
surge fares are
currently in effect?
Here’s a heatmap.
“I’m out driving—where should
I head next?”
While the Plan tab could help
drivers plan their strategy for
the day as a whole, map
overlays could give them the
real-time information they
need for deciding where to
head right now.
But as we’ve seen, just
showing surge areas is not
sufficient. Here’s a concept for
showing several different
types of information.

13. Prototyping

14. Legacy Design
While I believe Alerts and a Plan tab are both
concepts worth consideration, I decided to proceed
by prototyping the Map Overlays concept. I sense it
is the feature that would be used most often by the
largest number of drivers, is a logical evolution of
the existing interface, and would likely be able too
leverage existing app capabilities, minimizing the
engineering resources required to achieve it.
The Uber Driver App released in late 2015 included
a surge heatmap that could be shown and hidden
using a toggle. The control was situated alongside
a traffic toggle and location button at the top right
of the screen. Three buttons already risks cluttering
the interface; to add additional functions, we need
to consider displacing some of the controls.

15. Consolidate Controls
We’ll keep the location button, but consolidate the
surge and traffic toggles into a separate menu. This
displaced menu can be reached by tapping the
Google Maps-style layers icon.
Moving from a three-button design to a two-button
selection declutters the main interface while
expanding the functions we can offer the user.

16. Overlay Menu
The user can choose what type of information
they’d like presented on the map:
• Surge. The only visualization Uber currently offers.
• Fares. The average passenger fare. Takes into
account both surge and ride distance.
• Passenger ETAs. The average time it takes to
pick up the passenger once requested.
• Wait time. The average time after one ride is
completed before the next ride is assigned.
• Smart areas. Considers all of the above, highlight-
ing high-fare areas with low ETAs and waits.
• None. Turn off the active overlay.
Only one overlay can be displayed at a given time.

17. Surge Overlay
This shows the user where surge pricing is
currently in effect, just like the existing Uber app.

18. Surge Overlay
This time let’s select the “Fares” overlay…

19. Fares Overlay
Now we can see where the highest passenger
fares can be found.
Surge is a proxy for this, but fails to take into
account other factors such as journey distance.
Drivers would rather have a non-surge but long-
distance fare than a surge route that’s really short,
so my theory is that average fare could be a more
salient metric than surge. But that needs to be
validated/invalidated by talking to more drivers.

20. Fares Overlay
Data labels appear as the user zooms in, in this
case displaying the average fare in each area.

21. Fares Overlay
Let’s scroll through the other map overlays.
The “Passenger ETA” and “Average Wait Time”
overlays work just like the other two we’ve already
seen, so we won’t bother showing them here.
This time let’s select the “Smart” overlay.

22. Fares Overlay
Let’s scroll through the other map overlays.
The “Passenger ETA” and “Average Wait Time”
overlays work just like the other two we’ve already
seen, so we won’t bother showing them here.
This time let’s select the “Smart” overlay.

23. Smart Overlay
The smart overlay takes into account positive
indicators—like surge and average fare—but
balances them with negative indicators—such as
passenger ETA and wait time—to surface areas with
optimum risk vs. reward.
This guides drivers to places they can get “the most
bang for their buck” and, by extension, optimizes
the productivity of Uber’s workforce.
Next, let’s tap one of the tiles on the screen…

24. Area Detail
Every area tile is tappable. Tapping a tile reveals
key information about the selected area:
• Distance. How long will it take me to get there?
• Surge. Is a surge fare in effect?
• Fare. What is the average fare?
• ETA. How long does it take to reach passengers?
• Wait. What’s the time gap between rides?
From my conversations with Uber and Lyft drivers,
these are the key factors drivers are thinking about
when deciding where to drive. Putting this
information at their fingertips would help new
drivers get up to speed more quickly, and make
experienced drivers more productive.

25. Testing

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Next, I’d like to put this Map Overlay prototype in
front of drivers. Here are some of the areas in
which I would seek validation:
• Area details. What information do you care about
for a given area? My prototype assumes surge,
average fare, passenger eta, and wait time. Are
those all metrics you think about? Are there
others that are important to you?
• Map overlays. What information is it actually
helpful to see visualized on the map? Surge and
average fare seem important. But would you ever
use the ETA and wait time visualizations? Is there
another metric it would be helpful to visualize?
• Smart areas. Talk me through how you think
about the tradeoffs between high fares vs. bad
traffic, long ETAs, etc. When is it worth it?
Validation
?

27. Tyler Tate