How Google uses AI to reduce traffic and fight fuel emissions
Traffic lights can be annoying, but they also contribute to fuel emissions: a Google Research team wants to help fix this
Road transportation is responsible for significant global and urban greenhouse gas emissions. It’s especially problematic at city intersections where pollution can be 29 times higher than on open roads, and about half of these emissions come from traffic accelerating after stopping.
With millions of traffic lights across the world, the scale of the problem was huge — and if Google could do something to address it, so was the opportunity. So, when in 2020 team within Google Research was asked to explore new ideas for research projects that focused on accelerating climate mitigation, they came up with a rather briliant insight on traffic lights “where we stand at for no good reason”.
The mechanics of taffic engineering
With their curiosity sufficiently piqued, the team dug into the mechanics of traffic engineering. They found that while some amount of stop-and-go traffic is unavoidable, a portion can be prevented by optimizing traffic light timing. To do that, cities traditionally needed to either install expensive hardware or run time-consuming manual vehicle counts, neither of which provide complete information on key parameters they need.
It was quickly understood that Google has a strong advantage that cities could benefit from — over a decade of Google Maps driving trends from across the globe.
A few weeks later, a project proposal was ready. That proposal was for Project Green Light, an initiative that uses AI to make recommendations for city engineers to optimize existing traffic lights and reduce stop-and-go emissions. After evaluating dozens of other great ideas, Green Light was chosen for its simplicity, scalability and potential for impact.
Project Green Light
The Green Light team used Google Maps’ driving trends to create an AI model that measures how traffic flows through an intersection, including
- patterns of starting and stopping
- average wait times at a traffic light
- acoordination between adjacent intersections.
The model identifies possible improvements, like shaving off several seconds from a red traffic light during off-peak hours or an opportunity to coordinate between intersections that aren’t yet synced. The city’s engineers then review those recommendations and can implement them in as little as five minutes, using their city’s existing infrastructure.
“In order to achieve a positive climate impact, we want to be able to deploy high-quality Green Light recommendations to many cities globally and scale fast. So we purposely set up everything to be simple and lightweight — cities don’t need to invest in any dedicated software or hardware integrations,” says Green Light Program Manager Alon Harris. “We just share our recommendations with the city, and then they evaluate them and take action.”
Since their first pilot in 2021, the team has tested more and more intersections, developed more accurate predictions and took Green Light on the road to more than a dozen cities across the world, including Rio de Janeiro, Seattle, Bengaluru, and most recently, Boston. The team also developed a comprehensive dashboard to easily share recommendations and analytics with partner cities, while continuing to monitor for any new needed changes.
Green Light in practice
Today, Green Light is live in over 70 intersections, helping to save fuel and lower emissions for up to 30 million car rides monthly. Early numbers indicate the potential to reduce stops by up to 30% and reduce emissions at intersections by up to 10%.
The team is working to scale Green Light to hundreds of cities and tens of thousands of intersections in the next few years. And on that road to making the world a little better, they hope helping people experience less stop-and-go traffic will spread some joy along the way.
“You know when you drive through a sequence of five green lights, and it feels like your lucky day?” Dotan says. “We want to make that feeling more common to more people around the world.”
Not all right, thought
Google claims that its quietly rolled-out AI traffic signal project streamlines the process and reduces stoplight wait times — but it’s unclear exactly how helpful it really is.
Google’s Project Greenlight is, as Scientific American explains in this article, built on an algorithmic model that is supposed to be an alternative to the other two major systems of traffic signal control in use today.
While relying on either manually adjusted and fixed light changes or belowground sensors that let the systems know how many cars are at a given intersection, traffic design and control have long been a headache for urban planners. Enter Project Green Light, which uses a model known as adaptive or responsive traffic that works with Google Maps data to essentially train the system to guess when traffic gets worse and program stoplights accordingly.
Newly deployed in Boston, cities like Seattle, Machester, England, and others have used Project Green Light to varying degrees of success. However, anyone who has ever been in a fender-bender knows that sometimes, things happen on the road that can’t be predicted by an algorithm.
Bad advice
Representatives from the English city of Manchester, for instance, told SciAm that its traffic engineers often ignored Project Green Light’s recommendations regularly because they weren’t very good. Those same engineers had to manually set stoplights to prioritize things like bus routes, which the algorithms didn’t take into account, or to steer commuters away from driving through residential areas.
Even Mariam Ali, a spox for Seattle’s Department of Transportation who implemented Project Green Light and who generally spoke positively about the software, acknowledged its drawbacks. Though she told the magazine that the city has “seen positive results,” Ali admitted that the Seattle DOT had to reverse a Google-recommended traffic shift because it “did not result in a net benefit.”
So, in the end, it looks like that although it’s “great that Google is working” on implementing high-tech solutions for the headache-inducing quandaries presented by traffic signals, human decision-making will always be key. Not too bad.
sources: Google Blog I Futurism
cover image: Nabeel Syed via Unsplash
author: Barbara Marcotulli
Maker Faire Rome – The European Edition has been committed since its very first editions to make innovation accessible and usable to all, with the aim of not leaving anyone behind. Its blog is always updated and full of opportunities and inspiration for makers, makers, startups, SMEs and all the curious ones who wish to enrich their knowledge and expand their business, in Italy and abroad.
Follow us, subscribe to our newsletter: we promise to let just the right content for you to reach your inbox.
