The new findings were reported in a pair of papers in the journals Transportation Science and Transportation Research: Part B.

In these papers, MIT and Indian Institute of Technology - Bombay (IIT-Bombay) alumna Kanchana Nanduri and MIT assistant professor Carolina Osorio described a method of combining vehicle-level data with less precise -- but more comprehensive -- city-level data on traffic patterns to produce better information than current systems provide.

"What we do is develop algorithms that allow major transportation agencies to use high-resolution models of traffic to solve optimization problems," Osorio said.

For their test case, Osorio and Nanduri used simulations of traffic in the Swiss city of Lausanne, simulating the behaviour of thousands of vehicles per day, each with specific characteristics and activities.

"We came up with a solution that would lead to improved travel times across the entire city," Osorio noted.

In the case of Lausanne, this entailed modelling 17 key intersections and 12,000 vehicles. "The data needs to be very detailed, not just about the vehicle fleet in general, but the fleet at a given time," Osorio said.

"Based on that detailed information, we can come up with traffic plans that produce greater efficiency at the city scale in a way that's practical for city agencies to use," she pointed out.

 

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