The continuing increase in gasoline prices around the world over the past decade has also seen an increase in the practice of hypermiling – the act of driving using techniques that maximize fuel economy.
One of the most effective hypermiling techniques is maintaining a steady speed while driving instead of constantly stopping and starting. Unfortunately, traffic lights all too often conspire to foil attempts at keeping the vehicle rolling. Researchers at MIT and Princeton have now devised a system that gathers visual data from the cameras of a network of dashboard-mounted smartphones and tells drivers the optimal speed to drive at to avoid waiting at the next set of lights.
The new system, dubbed SignalGuru, was tested in both Cambridge, Massachusetts, and in Singapore. In Cambridge, where traffic signals are on fixed schedules, the researchers say the system was able to predict when lights would change with an average error of only two-thirds of a second and helped drivers cut fuel consumption by an average of 20 percent. In Singapore, where the duration of lights varies continuously according to changes in traffic flow, the error increased to an average of slightly more than one second, with one particularly light in densely populated central Singapore seeing an average error of more than two seconds.
The version of the system used in the tests graphically displayed the optimal speed for avoiding a full stop at the next light, but a commercial version would probably use audio prompts said Emmanouil Koukoumidis, a visiting researcher at MIT who led the project. The researchers also modeled the effect of instructing drivers to accelerate in order to catch lights before they changed, but decided that wasn’t the safest option.
“The good news for the U.S. is that most signals in the U.S. are dummy signals,” (signals with fixed schedules), says Koukoumidis, who launched the SignalGuru project at MIT with Li-Shiuan Peh, an associate professor in the Department of Electrical Engineering and Computer Science who came to MIT from Princeton in fall 2009. But Koukoumidis says even an accuracy of two and half seconds, “could very well help you avoid stopping at an intersection.” He also points out that the predictions for variable signals would improve as more cars were outfitted with the system, collecting more data.
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