Idling cars create unnecessary greenhouse gas emissions, so common sense indicates that less time sitting in traffic would lead to a cleaner world. A new study by researchers at MIT shows how reprogramming traffic signals could lead to fewer traffic jams and a world with lower carbon emissions.

mit research, traffic signal programs, traffic light programming, reducing carbon emissions, reducing greenhouse gas emissions, reducing traffic jams, urban planning, city planning, traffic management

Researchers used simulated modeling to apply a variety of algorithms to test the impact of different traffic signal programming on traffic flow and energy efficiency. For the experiment, researchers modeled traffic in the Swiss city of Lausanne, simulating the behavior of thousands of vehicles per day, each with specific characteristics and activities. The models also account for changes in driving behavior from day to day, such as the decision to take an alternate route when traffic is heavy on one’s usual path.

The new research is reported in a pair of papers written by by assistant professor of civil and environmental engineering Carolina Osorio and alumna Kanchana Nanduri SM ’13, and published in the journals Transportation Science and Transportation Research: Part B. Their research describes 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.

Related: U.S. set to blow $2.8 trillion by sitting in traffic

“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 says. “We came up with a solution that would lead to improved travel times across the entire city.”

Previous traffic signal programs can already simulate both city-scale and driver-scale traffic behavior, but other researchers encountered problems integrating the two. The MIT 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.

The team now is working on a project in Manhattan, among other locales, to test the potential of the system for large-scale signal control. In addition to programming traffic lights, simulations like this one could also be used in the future to optimize other planning decisions, such as picking the best locations for car- or bike-sharing centers, Osorio says.


Images via Shutterstock (1, 2)