Google’s Green Light: How 30M Drivers a Month Use AI Without Knowing It

Despite the woes, alarms, and fears trailing the increasing adoption of artificial intelligence (AI) in about every industry, there is no iota of doubt that AI is here to stay.

In fact, a McKinsey 2023 State of AI report shows that 55% of organizations have adopted AI for several usecases, and many more are poised to follow suit.?

While most discourse around AI these days centers on generative AI, Google, through its Green Light project, is drawing our attention to another use case that holds the potential to revolutionize traffic management in urban cities, which, by extension, can lead to a greener future.?

We can argue that the need for this project partly stems from the increasing number of commuters that have hit the roads since the Covid-19 pandemic was tamed.

With companies ordering their workforce back to the office, more people on the road, more urban traffic congestion, and a higher dose of greenhouse gas emissions being fed to the cloud.?

Key Takeaways

What is the Green Light Project and Why Does it Matter?

Google’s Green Light project is an urban traffic optimization initiative that employs AI and data from Google Maps driving trends to model traffic patterns to provide intelligent recommendations to city traffic engineers.

The project aims to cut down vehicle emissions and enhance urban mobility by optimizing traffic light timing configurations, minimizing unnecessary stops, and subsequently lowering greenhouse gas emissions.??

Explaining why Green Light is a great project, Fabio Botacci, Founder & CEO at VINCI Digital, an IIoT and GenAI Strategic Advisory firm, told Techopedia:

“The project is unique because it’s based on Google Maps traffic data, which means (potentially) petabyte of near-real-time data available to be sent to the cloud and analyzed at scale by AI/ML advanced algorithms, enabling and enhancing traffic lights actual synchronicity, and resulting in less waiting time/pollution and therefore increased urban sustainability.”

Statistics show that road transportation contributes significantly to global and urban greenhouse gas emissions. According to the United States Environmental Protection Agency, Greenhouse gas (GHGs) generated by the transportation sector constitute approximately 29% of the overall greenhouse gas emissions in the United States.

This makes transportation the primary source of GHG emissions in the country. The International Energy Agency, in a recent report, revealed that in 2022, the total global CO2 emissions from the transport sector increased by over 250 million metric tons of CO2, reaching almost 8 billion metric tons of CO2 in total. This marked a 3% rise compared to the emissions observed in 2021.?

The numbers above are scary given the urgency of global warming, which pushed world leaders to form the Paris Agreement, a blueprint for reducing emissions by 45% by 2030 and achieving net zero by 2025.?

How AI Powers Google’s Green Light Project

According to Google, Green Light focuses on reducing emissions by optimizing how traffic lights work at intersections. Yosil Matias, Google’s VP of Engineering and Research, wrote:

“About half of the emissions at intersections comes from traffic stopping and starting, and we found that by leveraging AI, we can reduce these emissions by optimizing traffic lights.”

One of the strengths of AI lies in its ability to help machines learn, adapt, and make decisions based on data for improved performance over time without extensive programming.

For the Green Light Project, AI is used to analyze extensive driving data from Google Maps to create AI models that capture details of traffic patterns at specific intersections. Included in these models are information regarding intersection structures, traffic flow dynamics, light scheduling, and the interactions between traffic and signals.

This detailed data forms the basis for traffic light optimization, as the AI identifies opportunities to synchronize lights for more efficient traffic flow. With this data, it becomes possible to recommend adjustments in light timing and coordinate multiple intersections simultaneously, thereby reducing the dependency on manual counts delivered through sensors.?

The Real-World Impact of Green Light

Green Light’s AI recommendations are designed to work with existing traffic infrastructure for easy implementation and integration with the city’s pre-existing systems.?

According to Google, the impact is quick and measurable, as city officials and engineers have reported potential reductions of up to 30% in stops and 10% in emissions at intersections where Green Light is operational. The initiative is already making a difference in many cities, including Abu Dhabi, Bali, Bangalore, Budapest, Haifa, Hamburg, Hyderabad, Jakarta, Kolkata, Manchester, Rio de Janeiro, and Seattle.?

Google further claims that Green Light is used to save fuel and lower emissions for up to 30 million car rides every month.

Grey Areas Amidst the Gains

Considering that every mention of AI exudes some breath of Orwellian-like suspicion, we spoke to some cybersecurity and AI experts to know if there are potential cybersecurity concerns in using Green Light.?

In a statement made available to Techopedia, Gary Huestis, owner and director of Powerhouse Forensics, argues that while the current scope of Green Light doesn’t lend itself to many security vulnerabilities, his primary concern centers on cities demanding access to the traffic data generated by Google.

“My biggest concern is that the goal of Google’s Green Light Project is to provide suggestions to cities on traffic signal timing, which could create the desire for the cities to have access to the data. This, in turn, could lead to the cities using that data against the drivers and eventually un-anonymizing the data and using it to generate traffic citations. The best way to address this is to keep the data anonymous and keep access to the raw data out of the cities and governments.”

Miguel Maloney Thompson, a security analyst at Loricca Inc., a Managed Security Service Provider, also raised concerns that Green Light’s collection of driving data poses a great risk to the cities as a collective and individuals, especially the public figures.

Thompson wrote in an email to Techopedia:

“I am seeing a possible issue with the collection of traffic data that could potentially lead to the tracking of individuals’ driving routes, their moving patterns, let’s say, someone’s daily or weekly routine, which raises privacy issues. We do not know if Google has implemented data anonymization, for example, to reduce these risks.”

In addition to privacy concerns, “there is the concern about security since this new type of data can attract hackers who could find ways to monetize the data of people’s whereabouts or movements. For example, a public figure or celebrity,” Thompson added.?

With the Green Light Project built to run on a lot of data, from traffic routines to people’s locations via Google Maps, there is a whole lot of data in question. The above informs the concern of Vaclav Vincalek, Founder of 555vCTO.

“There are definitely going to be ample security concerns with this system. It’ll be handling sensitive data and controlling traffic infrastructure, so that’s a lot of access to a lot of data. However, the bigger concern might be unauthorized access to the system, which could lead to control over traffic lights or the traffic management infrastructure.

 

If you’ve seen any movie where hackers have taken control of such systems, you can imagine the chaos it could cause on the streets. Anything from longer traffic jams to major accidents are potential threats.”

The Bottom Line

If you commute to work or drive around for pleasure, we can all agree that while the current traffic light configurations running on sensors provide mobility safety, it could be frustrating, especially when the red one shows up when there is no obstruction in sight.?

However, with the application of AI to the functionalities of traffic lights, we might witness a better driving experience in urban cities and a reduction in fuel spend and carbon emissions.

While the gains look promising on the periphery, there are question marks around how the project intends to handle privacy and security concerns.

Apart from the ones shared by some security experts we spoke to on this subject, I’m particularly concerned about how Green Light will make it possible for people to know that they are not being monitored and that the traffic data generated and processed by the AI model is solely to provide AI-driven recommendations to city engineers and only used for traffic light optimizations.?