dynamic pricing made familiar by Lyft
and Uber, which could in turn help
distribute demand, reduce congestion,
and increase capacity. Transit-agency
smartphone apps could also gather data
about what transportation researchers
refer to as the last-mile problem, by
identifying areas where people have
a hard time getting from the transit
stop to their destination. More data
surely could help inform planners and
reallocate resources so that more people
would benefit from public transit. But
what about gathering data on people
who don’t own a smartphone? Or
those who own a smartphone but who
have an income situation that results
in gaps in their pay-as-you go data
plans? Or people who don’t bother to
install a data-harvesting transit app
because they don’t have a credit card
or bank account to link for payment,
or because their phone is too old or too
full? Or who won’t trust a smartphone
app with their credit card number [ 2]?
These are otherwise invisible groups
of people overlooked when we throw
around statistics about smartphone
ownership. The people most likely to
be underrepresented in the data are
the most vulnerable—but they are
the very same people who stand to
gain the most from increased access to
public transportation. From an equity
standpoint, they are the ones society
should be investing in. But if the data
that should form the basis for the
decision can’t be gathered, then there’s
no way to make an equitable decision.
There are positive examples of
smartphone technology helping the
blind, for example [ 3]. And the jury is
still out on the cumulative effect of the
widespread adoption of information
and communication technology on
transit [ 4]. It’s tempting to add dynamic
smartphone data to the comparatively
static data that has been the focus of
previous efforts to optimize transit-network design [ 5], in an effort to
improve the user experience for middle-class customers of public transportation
systems. But this could have the effect
of reducing the relative amount of data
we have on working-class customers.
This data gap is a problem in many other
contexts, and an especially troubling
one because it could have the tendency
screams out for the capabilities of
smartphones. This conundrum is at
the heart of a recent call for research
proposals from the National Institute
for Transportation and Communities,
and it’s worth thinking about as the
Internet of Things and technology
in general continue to transform our
built environment. For example, one
issue is that public transportation,
like most other things related to
settlement patterns, functions far
more efficiently at higher densities.
Density makes things like subways,
frequent service, and dedicated lanes
for bus rapid transit practical. One of
the best ways to encourage density is to
charge people more money for traveling
longer distances. To some degree this
disincentive is built into the cost of car
travel; choose a longer commute and
you’ll pay more in gas and insurance.
But it’s been long established that
the problematic flat fare is the norm
for most urban public transportation
systems in the U.S. [ 1]. Since it’s usually
less expensive to live farther from
the city center, a flat fare creates an
economic incentive for people to move
farther away. This means the transport
provider must transport more people
over longer distances while generating
no additional fare revenue—and you
don’t need an MBA to know that’s
not an appealing business model.
Furthermore, flat fares tend to decrease
the capacity of transit systems at peak
hours because the same seat that could
serve two or three short-haul passengers
is occupied by one heading all the way
out to the last stop on the line.
Distance-based fares are one answer
to these interconnected problems; many
systems in Europe and Asia operate
on this principle. But as tourists can
attest, these can be confusing when they
operate with paper-based ticket systems
because they require the user to consult
a confusing matrix when traveling
from one outer zone to another: If I’m
going from zone 6 to zone 3, but passing
through zone 1, do I need a three-zone
or a six-zone ticket? This usability
problem is simple to solve with the
graphical interface of a smartphone.
Furthermore, smartphone-based
fare systems could open up a world
of possibilities, such as the type of
to accelerate the widening gap in equity.
Relying on smartphone data raises the
same potential issues of invisibility as
the AI call-sorting system. It’s easy
to believe the world you know—and
to collect data that reinforces these
beliefs. It is considerably more difficult
to collect data about that which is not
familiar.
This brings me back to the connected
speaker on my bathroom counter. Never
before have my expectations of what
the world around me can do shifted so
quickly. For those of us in the middle
class, it’s easy to think of a hyper-connected world as the new normal. But
it’s not. It’s important to remember that
many in the U. S., and most in the world,
do not enjoy the same uninterrupted
access to a steady stream of income nor
the constant connectivity that money
enables. As we strive to make life more
convenient, enjoyable, and productive
for those so privileged, we should make
sure that we are not pulling the plug on
our less-fortunate neighbors.
Endnotes
1. Cervero, R. Flat versus differentiated
transit pricing: What’s a fair fare?
Transportation 10, 3 (Sept. 1981), 211–232.
2. Shirgaokar, M. Expanding seniors’
mobility through phone apps: Potential
responses from the private and public
sectors. Journal of Planning Education and
Research (Apr. 2018).
3. Campbell, M., Bennett, C., Bonnar, C.,
and Borning, A. Where’s my bus stop?
Supporting independence of blind transit
riders with StopInfo. Proc. of the 16th
International ACM SIGACCESS Conference
on Computers & Accessibility. ACM, New
York, 2014, 11–18.
4. Shaheen, S. and Cohen, A. Is it time for
a public transit renaissance? Navigating
travel behavior, technology, and business
model shifts in a brave new world. Journal
of Public Transportation 21, 1 (Jan. 2018).
5. Ram, S., Wang, Y., Currim, F., Dong, F.,
Dantas, E., and Sabóia, L. A. SMARTBUS:
A web application for smart urban
mobility and transportation. Proc. of the
25th International Conference Companion
on World Wide Web. ACM, New York,
363–368.
Jonathan Bean is assistant professor of
architecture, sustainable built environments,
and marketing at the University of Arizona.
He researches domestic consumption,
technology, and taste.
→ j.bean@arizona.edu
INTERACTIONS.ACM.ORG JANUARY–FEBRUARY 2019 INTERACTIONS 23
DOI: 10.1145/3297678 COP YRIGHT HELD BY AUTHOR