A start-up called Urban Engines believes data analysis and commuter lotteries can help cities reduce congestion.
By Eric Jaffe, May 15, 2014
You can think of rush-hour traffic as a fairly straightforward problem
of supply and demand. The supply is the capacity on city buses, trains,
and roads. The demand is the share of metro area commuters who want to
ride transit or drive cars. Congestion at the morning and evening peaks
is a sign of demand overwhelming supply: too many commuters trying to
use buses and trains and roads at the same time.
That's how the founders of Urban Engines,
a congestion-relief start-up making its public launch today, prefer to
view the city traffic problem. It's also the key to the company's
proposed solution. On one hand, they believe new data insights can help
cities adjust the supply of transit vehicles and road space. On the
other, they plan to use behavioral incentives to control commuter
"That's us in a nutshell: insights and incentives to attack
congestion," says CEO and co-founder Shiva Shivakumar, a former Google
engineer. "The better you can understand both sides of the
[supply-demand] equation, the better you can start optimizing it."
On the insight side, Urban Engines relies on an approach called
"crowd-sensing" to understand what's happening across an entire city
transport system. Let's take the example of a subway. Each fare card
entry swipe delivers basic information on rider location and (at least
for cities that require a swipe in and out) total travel time. Using
algorithms and supplemental data, such as real-time transit schedules,
Urban Engines can deduce what's happening at any given subway station or
train at any given time.
"Every single person in a crowd becomes a mini sensor," says co-founder Balaji Prabhakar,
a Stanford professor of computer science. "Their overall trip plan
actually tells us, when taken together, what's happening in the system."
That's a big improvement over the small platform or on-board samplings
some cities currently use to get a sense of system flow. As a result,
Urban Engines can produce interactive data visualizations that give
short-term congestion insights (this platform is overcrowded, trains on
this line are bunching) and longer-term traffic trends (on rainy days
this station needs more cars). Transit operators can use that
information to scheduled and dispatch train supply more efficiently.