It might be hard for Uber to convince drivers and passengers to carpool, but a study from MIT says widespread use of ridesharing with a good algorithm could cut traffic in crowded cities way, way down.
Researchers used modelling to test a new algorithm on three million taxi trips in New York City. By using carpooling and matching rides in real-time, MIT found just 3,000 cars could cover 98 percent of those rides while keeping wait time to an average 2.7 minutes.
"To our knowledge, this is the first time that scientists have been able to experimentally quantify the trade-off between fleet size, capacity, waiting time, travel delay and operational costs for a range of vehicles, from taxis to vans and shuttles," MIT computer science professor Daniela Rus said in a press release.
Uber and Lyft already do a version of this, researchers said, but the MIT model essentially puts all riders in a carpool and makes the logistics of that pool more complex than those services currently allow.
Researchers tested their algorithm using one of the most dense areas of the country. Your city's mileage may vary, but the benefits of algorithmic carpooling could add up. Traffic costs an estimated $160 billion a year in lost time and wasted gas, plus there's a potentially huge environmental benefit to getting more cars off the road.
Of course, as with most ride-sharing news, this is bad for taxis: researchers said they could cover most demand with just a couple hundred cars, compared to the roughly 14,000 taxis operating in New York City now.
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