Due to its successful application in cities such as London and Singapore, congestion charging has become the favored approach for changing driver behavior. However, a professor at Stanford University may have found a nicer way to change habits.
With efforts to impose congestion pricing in the United States having failed thus far, most prominently in New York City, Balaji Prabhakar, a professor of computer science at Stanford University may have found an incentive-based program to accomplish similar goals, reports John Markoff.
Choosing the carrot over the stick, Stanford deployed a new system designed by
Dr. Prabhakar's group this spring with the aim of reducing traffic congestion at peak hours. "Called Capri, for Congestion and Parking Relief Incentives,
it allows people driving to the notoriously traffic-clogged campus to
enter a daily lottery, with a chance to win up to an extra $50 in their
paycheck, just by shifting their commute to off-peak times."
"Dr. Prabhakar is a specialist in designing computer networks and has conducted a variety of experiments
in using incentives to get people to change their behavior in driving,
taking public transit, parking and even adopting a more active
lifestyle. Unlike congestion pricing,
which is mandatory for everyone and usually requires legislation,
"incentives can be started incrementally and are voluntary," he said."
"Moreover, systems based on incentives can offer a huge advantage in
simplicity. Until recently, the Stanford system required sensors around
campus to detect signals from radio-frequency identification tags that
participants carried in their cars. But the need for such an
infrastructure has vanished now that so many drivers carry smartphones
with GPS chips or other locaters."
While Markoff notes that Stanford plans to expand the popular program soon to cover parking on campus, experts disagree on whether an incentives-based system could work as well in a city such as New York or San Francisco.
Read the full story: Incentives for Drivers Who Avoid Traffic Jams
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