Here’s a weird one: researchers with the Massachusetts Institute of Technology (MIT) believe that wholesale reprogramming of city traffic signals could play a critical role in cutting greenhouse gas [GHG] emissions from motor vehicles, while also improving transportation efficiency in the bargain.That’s the conclusion reached in a pair of papers by assistant professor of civil and environmental engineering Carolina Osorio and MIT alumna Kanchana Nanduri, published recently in Transportation Science and Tra
In those papers, MIT said the authors theorize that combining vehicle-level data with less precise — but more comprehensive — city-level data on traffic patterns can produce better information than current systems provide.
Osorio believes taking that “more comprehensive” data and then plugging into new algorithms can allow major transportation agencies to use higher-resolution models of traffic to better “optimize” roadway usage, thus reducing traffic bottlenecks and emission levels simultaneously.
“Typically, traffic signal timing determinations are set to optimize travel times along selected major arteries, but are not sophisticated enough to take into account the complex interactions among all streets in a city,” Osorio added.
“In addition, current models do not assess the mix of vehicles operating on a road at a given time, so they can’t predict how changes in traffic flow may affect overall fuel use and emissions,” she stressed.For their test case, Osorio and Nanduri crafted traffic simulations based on Swiss city of Lausanne, simulating the behavior of thousands of vehicles per day, each with specific characteristics and activities.
Their model even accounts for how driving behavior may change from day to day; for example, noting how changes in traffic signal patterns can make a given route slower, thus causing people to choose alternative routes on subsequent days.
While existing programs can simulate both city-scale and driver-scale traffic behavior, integrating the two has been a problem. That;s why the MIT researchers think that reducing the amount of detail sufficiently to make the computations practical, while still retaining enough specifics to make useful predictions and recommendations, is the way to go.
“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 noted. “We came up with a solution that would lead to improved travel times across the entire city.”In the case of Lausanne, their model involved 17 key intersections and 12,000 vehicles, while incorporating 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 pointed out. “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.”
In short, Osorio believes merging complex data with less-detailed data to create more “computer-friendly” solutions that result in more practical fixes.
“Agencies are now being asked, whenever they propose changes, to estimate what impact that will have environmentally,” she noted. “Currently, such evaluations need to be made after the fact, through actual measurements. But with these new software tools, we can put the environmental factors right into in plan’s design loop.”
To that end, MIT said its team is now working on a project in Manhattan, among other locales, to test the potential of the system for large-scale signal control.
In addition to timing traffic lights, future simulations could also be used to optimize other planning decisions, such as picking the best locations for car- or bike-sharing centers, Osorio stressed.
Who knew all that could result from merely re-programming traffic lights? Yet the question is; will it work as theorized in the real world? That’ll be the big trick in the end.