Better traffic signals can cut greenhouse gas emissions

Analysis shows that smarter programming of stoplights could improve efficiency of urban traffic.

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Sitting in traffic during rush hour is not just frustrating for drivers; it also adds unnecessary greenhouse gas emissions to the atmosphere.

Now a study by researchers at MIT could lead to better ways of programming a city’s stoplights to reduce delays, improve efficiency, and reduce emissions.

The new findings are reported in a pair of papers by assistant professor of civil and environmental engineering Carolina Osorio and alumna Kanchana Nanduri SM ’13, published in the journals Transportation Science and Transportation Research: Part B. In these papers, the researchers describe 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,” Osorio says, “is develop algorithms that allow major transportation agencies to use high-resolution models of traffic to solve optimization problems.” Typically, such 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. In addition, 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 Nanduri used simulations of traffic in the Swiss city of Lausanne, simulating the behavior of thousands of vehicles per day, each with specific characteristics and activities. The model even accounts for how driving behavior 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 programs can simulate both city-scale and driver-scale traffic behavior, integrating the two has been a problem. 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.

“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.” In the case of Lausanne, this entailed modeling 17 key intersections and 12,000 vehicles.

In addition to optimizing 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 says. “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 says, “We take complex data and couple that with less-detailed data [to create] computer-friendly solutions that combine the two kinds of data to come up with practical solutions.”

Osorio adds, “Agencies are now being asked, whenever they propose changes, to estimate what impact that will have environmentally.” Currently, such evaluations need to be made after the fact, through actual measurements, but with these new software tools, she says, “We can put the environmental factors in the loop in designing the plan.”

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 timing traffic lights, in the future such simulations could also be used to optimize other planning decisions, such as picking the best locations for car- or bike-sharing centers, Osorio says.

Kai Nagel, a professor at the Technical University of Berlin who was not involved in this research, says this work “ties together the realism of detailed traffic microsimulators with the rigor of mathematical approximation models. The approach is both very sound and very recent. It is, even in the academic realm, a true innovation.”

Nagel adds that this research “addresses one of the most important problems faced by cities of today and the future,” and says these new findings open up “a wealth of future research opportunities.”

Topics: Research, Algorithms, Traffic management, Transportation, Pollution, Urban planning, Climate change, Sustainability, Analytics, Greenhouse gases, School of Engineering, Civil and environmental engineering


Many years ago I requested an assemblyman friend to have the California Transportation authority eliminate unnecessary stops on freeway on ramps. My suggestion was default the entrance signal to green to allow single cars to enter the freeway without stopping and to turn the signal to red for a short interval afterwards. This went nowhere leading me to believe that the highway managers want more gasoline used.
Another fuel saver would be to eliminate stop signs and put yield signs in their place. Of course this would cut down on city income from violators so this will probably never happen either.

We have known of most of this since the 1950s. We have had computers capable of helping since the 1970s. Some cities and suburbs have major computerized traffic signal coordination.
The dream improved when wireless emerged: If cars can declare their intended routes, the system can use that information to improve timing and economy for everyone. If cars can "listen" for advice, the system can suggest small adjustments to speed and route, yielding even better improvement. Safeguards are needed: Jamming can remove benefits. Hacking can cause huge problems (sabotage). Adding optical signalling (line-of-sight infrared) in parallel with radio could add robustness. (Optical jamming range is shorter.) The system can detect faulty GPS readings or reports by proximity to actual base stations.
Additional benefits are possible if cars can talk to each other, but cars are more hackable than "the system", so vastly more caution is needed (unbreakable encryption and identification, and central authentication with verification against a reputation blacklist). Cars could form ad-hoc wireless networks for connectivity to the system, greatly reducing infrastructure cost. (But the relayed signals must be impervious to man-in-the-middle attacks.) Car-to-car declarations of emergency stops can be especially dangerous if forged, and present an opposite danger if they are omitted when relied upon. A car should identify the cars in front of it and authenticate before being ready to trust an emergency declaration from it.

They already have systems that do this. For example, in southeast Denver, East Arapahoe Rd, east of the freeway, and connecting roads north thereof, towards Aurora. Visiting in July 2013, i found that EVERY traffic signal in the area was timed to stop me just as I got to it. For days straight, it NEVER failed. I had to wonder, is there a special transmitter in my car, or do they do this to every car here? And Why? Pull out fast or pull out slow, stop at the next signal. 10 mph below the speed limit or 10 mph over the speed limit, stop at the next signal. (Seeing this, they'll probably mail me a ticket.) See a red light ahead, so slow down a bit to avoid stopping; it turns green; speed up, and it turns red as I get to it - DAMN! This is the new, moneyed side of town - is this a deterrent against outsiders even to tour or even visit the area? In the tireder, poorer northwest of the city (demonstrated by cheap motels and a rare closed McDonalds), the traffic lights did not jam me up the same way. Yes I stopped, but it seemed about typical luck basis, not Every Damn Time.

I read all the article and I think that it is very interesting!!! Congrats for Miss Osorio!

Please help!! I'm an MIT grad, and I drive to Newark, NJ to take the train everyday. There are obvious commuting patterns that clearly the traffic lights there are oblivious to. The example here is "McCarter Hwy (Route 21)" (but it could be anywhere). The pattern is that in the morning, many cars come from the interstate (I-78) and drive towards the train station (Newark Penn). In the afternoon, the pattern is exactly the reverse. Adding to that, you have some office workers heading to and from downtown Newark that follow a similar pattern. Currently, the lights are programmed to turn green along a stretch of McCarter Hwy simultaneously, which has no preferred direction and does not vary with the time of day or the day, or the day of the week in any noticeable way. This setup is clearly sub-optimal. Intuitively, timing the lights to allow for a higher flow rate in the direction preferred by traffic would be a vast improvement. From what this article says, your analytics would save us commuters a lot of time. I use waze everyday, so ideally, these companies should share their traffic data (Waze is owned by Google).

I got to say, i have also lived in Manhattan, which is a lot bigger and more challenging, because the patterns are more patchy and are more symmetrical on the grid (perhaps the areas near bridges or tunnels would have the most predictable patterns).

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