A new analytic framework enables analysis of GPS data on 150 million cab rides in New York City.
Cellphone apps that find users car rides in real time are exploding in popularity: The car-service company Uber was recently valued at $18 billion, and even as it faces legal wrangles, a number of companies that provide similar services with licensed taxi cabs have sprung up.
What if the taxi-service app on your cellphone had a button on it that let you indicate that you were willing to share a ride with another passenger? How drastically could cab-sharing reduce traffic, fares, and carbon dioxide emissions?
Authoritatively answering that question requires analyzing huge volumes of data, which hasn’t been computationally feasible with traditional methods. But in today’s issue of the Proceedings of the National Academies of Sciences, researchers at MIT, Cornell University, and the Italian National Research Council’s Institute for Informatics and Telematics present a new technique that enabled them to exhaustively analyze 150 million trip records collected from more than 13,000 New York City cabs over the course of a year.
Their conclusions: If passengers had been willing to tolerate no more than five minutes in delays per trip, almost 95 percent of the trips could have been shared. The optimal combination of trips would have reduced total travel time by 40 percent, with corresponding reductions in operational costs and carbon dioxide emissions.
“Of course, nobody should ever be forced to share a vehicle,” says Carlo Ratti, professor of the practice in MIT’s Department of Urban Studies and Planning (DUSP) and one of the paper’s coauthors. “However, our research shows what would happen if people have sharing as an option. This is more than a theoretical exercise, with services such as Uber Pool bringing these ideas into practice.”
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via Bing News
