CAMBRIDGE, Mass. — The rise of self-driving cars is set to dramatically alter the way we move around cities in the future.
In particular, private car ownership is expected to shift toward shared mobility services, with vehicle fleet operators offering on-demand transportation. This should help to reduce traffic in urban areas and cut greenhouse gas emissions.
For these services to grow, however, accurate and computationally efficient algorithms will be needed to effectively match individuals with on-demand vehicles, in order to cope with the hundreds of thousands of trips that are routinely made within large cities.
But researchers have yet to solve the problem of how best to size and operate a fleet of vehicles, given a particular level of demand for personal mobility.
Now, in a paper published today in the journal Nature, a team of researchers coordinated by Carlo Ratti, director of MIT’s Senseable City Lab, unveil a computationally efficient solution to this problem, which they dub the “minimum fleet problem.”
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