Reducing Highway Congestion Through Software
Professor co-leads $1 million study
Traffic congestion is a waste of time, energy, and money. In 2011, it caused Americans in metropolitan areas to spend 5.5 billion extra hours on the road and pump 2.9 billion extra gallons of fuel into their gas tanks, with associated costs reaching $121 billion—a nearly sixfold increase since 1982. Municipalities have attempted to mitigate traffic congestion through highway on-ramp metering and fees at peak travel times, but the problem continues to worsen.
Now a research team led by Calin Belta, a Boston University College of Engineering professor of mechanical engineering and systems engineering, and Murat Arcak, an associate professor at University of California, Berkeley, is advancing a novel solution that could considerably reduce traffic congestion. Supported by a three-year, $1 million grant from the National Science Foundation, the researchers plan to develop algorithms for a data-driven traffic management software system that optimizes the timing of traffic lights at both highway on-ramps and roadway intersections in real time.
The work represents a novel application of “formal methods,” a discipline within computer science focused on efficient techniques for proving the correct operation of systems ranging from computer programs to digital circuits, thus ensuring their reliability and robust performance.
“We want to develop a system in which we can guarantee specifications for traffic networks just as we do for computer programs,” says Belta. “These specifications will include minimizing traffic jams and maximizing the flow of traffic, all while ensuring that pedestrians don’t have to wait a long time to cross the street.”
Whereas current systems reduce traffic congestion within small networks of freeways and arterial roads, this approach promises to do so across much larger networks. In their algorithms, the researchers plan to partition a large road network into small sub-networks and establish specifications so that enforcing desired traffic patterns in small sub-networks (and on roads linking one sub-network to another) guarantees desired traffic patterns in the original network.
The proposed techniques will be tested in current and upcoming traffic management projects in California sponsored by Caltrans, the state transportation agency. Applications include a prototype decision support system to be deployed along the Interstate 210 corridor north of Los Angeles, and coordinated ramp metering, arterial intersection, and variable speed limit management on a freeway in Sacramento and a freeway-arterial interchange in San Jose.
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