A new smart traffic system could dramatically cut the time drivers are stuck at signals - and the amount of carbon emission caused by red lights, MIT scientists, including one of Indian-origin, have found.
Researchers 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 optimisation problems," said Carolina Osorio, assistant professor at Massachusetts Institute of Technology (MIT).
Typically, such timing determinations are set to optimise travel times along selected major arteries, but are not sophisticated enough to take into account the complex interactions among all streets in a city.
Current models do not assess the mix of vehicles on the road at a given time - so they can't predict how changes in traffic flow may affect overall fuel use and emissions.
For their test case, Osorio and MIT alumna Kanchana 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.
The model even accounts for how driving behaviour may change from day to day: For example, changes in signal patterns that make a given route slower may cause people to choose alternative routes on subsequent days.
While existing programmes can simulate both city-scale and driver-scale traffic behaviour, integrating the two has been a problem.
The team found ways of reducing the amount of detail sufficiently to make the computations practical, while still retaining enough specifics to make useful predictions and recommendations.
"With such complicated models, we had been lacking algorithms to show how to use the models to decide how to change patterns of traffic lights," Osorio said.
"We came up with a solution that would lead to improved travel times across the entire city," said Osorio.
In the case of Lausanne, this entailed modelling 17 key intersections and 12,000 vehicles.
In addition to optimising travel times, the new model incorporates specific information about fuel consumption and emissions for vehicles from motorcycles to buses, reflecting the actual mix seen in the city's traffic.
"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," Osorio added.
The research was published in the journals Transportation Science and Transportation Research: Part B.
