Better Traffic Light Simulations Could Cut Travel Time and Gas Use

More complex algorithms could cut travel time by 20 percent

Photo: Jose-Luis Olivares/MIT
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If you’ve ever sat in city traffic, burning time and gasoline, you’ve probably wondered why there isn’t a better way to time the lights.

Researchers at Massachusetts Institute of Technology (MIT) are also mulling the issue, and doing something about it. There are already sophisticated traffic models out there, and there are also detailed fuel consumption models. But the two are rarely used in tandem to cut down on travel times and gas use.

Carolina Osorio, an assistant professor of civil and environmental engineering at MIT, simulated microscopic models of traffic and fuel consumption for the Swiss city of Lausanne. The microscopic models incorporate data down to the individual driver. The findings will appear in a forthcoming issue of the journal Transportation Science [PDF].

More sophisticated traffic light algorithms are needed across the globe as urban populations expand. The International Energy Administration projects that fuel use for road transportation could double between 2010 and 2050 if strong efficiency measures are not implemented. Traffic doesn’t just suck up extra gas and produce smog, it also cuts down on productivity as people waste time getting to their destination.

Cities do have different simulators and algorithms for their traffic lights. The most common are large-scale, flow-based models, while others break travel patterns down to individual vehicles.

The problem with microscopic simulators for traffic light networks is that the outcomes are largely non-linear and require a large number of replications to get accurate results, which can drive up the cost of doing the simulation.

“What if we combine information from these microscopic simulations with [citywide] information from these simple traffic models that are very computationally efficient and run instantly, but have very low resolution?” asked Osorio.

Osorio’s research involves a simulation-optimization technique that requires fewer simulation runs, so that it is more cost effective. The study examined the first hour of evening rush hour traffic across 47 roads and 15 intersections in Lausanne. Nine of those intersections have traffic lights, which run on either 90 or 100-second cycles. The simulation was able to reduce travel time by 22 percent compared to commercially available traffic light timing software.

Info: This figure shows two maps with colored lines that represent the main roads in Lausanne, Switzerland. The three colors represent how long it takes to commute: red is the longest commute, yellow is average, and green is the shortest commute. The left map, with conventional traffic light programming, has many red lines that represent long commutes. The right map, which uses the researcher's improved system, has many green lines that represent short commutes.

“Usually in practice, when you want to time traffic lights, traditionally it’s been done in a local way,” Osorio said in a statement. “What is less done, and is more difficult to do, is when you look at a broader scale, in this case the city of Lausanne, and you want to change signal times at intersections distributed across the entire city, with the objective of trying to improve conditions across the entire city.”

Other research wants to move beyond just simulating vehicle patterns and having cars communicate with the road infrastructure to improve smart traffic light systems.

For now, however, cities are happy just to improve the models they are already working with. New York City’s Department of Transportation is collaborating with MIT to test the model. It already uses traffic algorithms and even microwave sensors to detect traffic in certain intersections.

“Such a model can validate our active traffic-management system in Manhattan, and allow us to fine-tune our processes and improve the network operation,” Mohamad Talas, a deputy director of system engineering for NYC’s DOT, said in a statement. “I believe that this approach is economically viable, with cost savings for any jurisdiction that needs to assess and improve traffic conditions for a large area of the transportation network.”

 

Illustration: MIT

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