Interactions - January/February 2019

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.