Where previous experimental traffic-light advisory systems used GPS data or data from traffic sensors, SignalGuru uses visual data from cellphone cameras. Image: Christine Daniloff
from Massachusetts Institute of Technology (MIT) and Princeton University
developed a system that uses a network of smartphones mounted on car dashboards
to collect information about traffic signals and tell drivers when slowing down
could help them avoid waiting at lights. By reducing the need to idle and
accelerate from a standstill, the system saves gas: In tests conducted in Cambridge, Mass.,
it helped drivers cut fuel consumption by 20%.
are responsible for 28% of the energy consumption and 32% of the carbon dioxide
emissions in the United
States, says Emmanouil Koukoumidis, a
visiting researcher at MIT who led the project. “If you can save even a small
percentage of that, then you can have a large effect on the energy that the U.S. consumes,”
system is intended to capitalize on a growing trend, in which drivers install
brackets on their dashboards so that they can use their smartphone as a GPS
navigator while driving. But unlike previous in-car cellphone applications, the
new system, dubbed SignalGuru, relies on images captured by the phones’
cameras. According to Koukoumidis, the computing infrastructure that underlies
the system could be adapted to a wide range of applications: The camera could,
for instance, capture information about prices at different gas stations, about
the locations and rates of progress of city buses, or about the availability of
parking spaces in urban areas, all of which could be useful to commuters.
Fixed or flexible?
In addition to testing SignalGuru in Cambridge,
where traffic lights are on fixed schedules, the researchers also tested it in Singapore,
where the duration of lights varies continuously according to fluctuations in
traffic flow. In Cambridge,
the system was able to predict when lights would change with an error of only
two-thirds of a second. In suburban Singapore, the error increased to slightly
more than a second, and at one particular light in densely populated central
Singapore, it went up to more than two seconds. “The good news for the U.S.,” Koukoumidis says, “is that most signals
in the U.S.
are dummy signals”—signals with fixed schedules. But even an accuracy of two
and half seconds, Koukoumidis says, “could very well help you avoid stopping at
an intersection.” Moreover, he points out, the predictions for variable signals
would improve as more cars were outfitted with the system, collecting more
Theory into practice
The researchers did model the effect of instructing drivers to accelerate in
order to catch lights before they changed, but “we think that this application
is not a safe thing to have,” Koukoumidis says. The version of the application
that the researchers used in their tests graphically displays the optimal speed
for avoiding a full stop at the next light, but a commercial version,
Koukoumidis says, would probably use audio prompts instead.
envisions that the system could also be used in conjunction with existing
routing software. Rather than recommending, for instance, that a car slow to a
crawl to avoid a red light, it might suggest ducking down a side street.
is a great example of how mobile phones can be used to offer new transportation
services, and in particular services that had traditionally been thought to
require vehicle-to-vehicle communication systems,” says Marco Gruteser, an
associate professor of electrical and computer engineering in the Wireless
information Network Laboratory at Rutgers University. “There is a much more
infrastructure-oriented approach where transmitters are built into traffic
lights and receivers are built into cars, so there’s a much higher technology
obstacle to commercial deployment of the system, Gruteser says, could be “finding a way to get the participation numbers required for this type of
crowd-sourcing solution. There’s a lot of people who have to use the system to
provide fresh sensing data.” Additional traffic-related applications, of the
type that Koukoumidis is investigating, could be one way to drive
participation, Gruteser says, but they won’t emerge overnight. “The processing
algorithms would be a little more complex,” Gruteser says.