Of all the aspects of pre-pandemic life people missed, sitting in traffic was not one. If you’ve returned to the office full- or part-time, you might have noticed it creeping back into your life. Traffic jams are not only annoying — the National Renewable Energy Laboratory calculated that each of us on average spends 40-50 hours in them each year — they waste a lot of energy, about 3.3 billion gallons of fuel annually. And all that time spent staring at taillights adds up to 8.8 billion hours and $10 billion of lost productivity per year. NREL wants to do something about that, so it put Eagle, a supercomputer that can make 8 million-billion calculations per second, to the task.
For a number of reasons — it’s not far from the Department of Energy’s Oak Ridge National Laboratory, it’s in the top 20 traffic-congested cities in the nation, and it’s a medium-sized metro area — NREL chose Chattanooga, Tenn., as its test case. NREL scientists created a digital twin of Chattanooga’s traffic patterns and set Eagle to work optimizing flow. The time- and gas-saving techniques it examined could be used in cities across the U.S. to cut down on commutes and reduce fuel consumption by as much as 20%.
Optimized Traffic Lights
The quickest fix NREL and Eagle found, using machine learning and data from GPS and vehicle sensors, was that traffic lights in a high-traffic area were timed for rush hour and not adjusted during off-peak times. That resulted in large chunks of the day when cars in lighter traffic were stopping at red lights for no real reason.
Diese Geschichte stammt aus der July 2021-Ausgabe von The BOSS Magazine.
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Diese Geschichte stammt aus der July 2021-Ausgabe von The BOSS Magazine.
Starten Sie Ihre 7-tägige kostenlose Testversion von Magzter GOLD, um auf Tausende kuratierte Premium-Storys sowie über 8.000 Zeitschriften und Zeitungen zuzugreifen.
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