How Google’s Green Light Uses AI to Improve Traffic and Lower Emissions

Google is using artificial intelligence to tackle one of the most overlooked sources of urban pollution—traffic lights. Through Project Green Light, cities around the world are seeing reduced congestion, lower emissions, and more efficient traffic flow.

Reducing Emissions at Urban Intersections

Urban intersections are among the most emission-heavy zones in cities, with pollution levels up to 29 times higher than on open roads. Project Green Light targets this problem by optimizing signal timings using AI-powered insights, reducing vehicle idle time and stop-and-go traffic—major contributors to city-level emissions.

Drawing on years of anonymized Google Maps data, the system generates traffic timing recommendations that help city engineers adjust signal patterns without the need for costly infrastructure upgrades.

A Practical Response to a Common Frustration

Project Green Light emerged from Google Research’s broader climate tech explorations in 2020. The initiative initially explored a wide range of mitigation strategies before identifying urban traffic management as a space with immediate, cost-effective opportunities—commonly referred to as “low-hanging fruit.”

The concept also received a personal push from within the company when a Google engineer noted the widespread frustration caused by inefficient traffic lights. This everyday inconvenience became the starting point for a system designed to deliver measurable improvements in traffic flow and environmental impact.

Google Research leadership aligned around a clear goal: reduce unnecessary vehicle stops at intersections, limit fuel waste, and lower carbon emissions using smart, scalable solutions.

How the System Works

Traditional traffic optimization methods often rely on physical sensors, manual traffic counts, or hardware upgrades. These approaches are not only expensive but also limited in scope and flexibility.

Project Green Light bypasses these limitations by using machine learning models trained on historical and real-time traffic data. These models analyze vehicle movement patterns, including acceleration, waiting times, and signal coordination. Based on this, the system delivers actionable, intersection-specific recommendations such as:

• Shortening red lights during low-traffic hours
• Synchronizing adjacent traffic signals
• Reducing unnecessary stops across major routes

These suggestions are designed to be easy to implement. In many cases, local traffic teams can integrate them into existing traffic management systems within minutes, using no new hardware or software.

The system also offers a performance dashboard, giving city officials access to traffic trends, adjustment suggestions, and post-implementation results to monitor effectiveness.

Measurable Success Across Global Cities

Since its pilot phase in 2021, Project Green Light has scaled to 17 cities, optimizing more than 70 intersections worldwide. Participating cities include Boston, Bengaluru, Rio de Janeiro, Haifa, Hamburg, and Manchester.

Early data shows the system can reduce vehicle stops at intersections by up to 30%, with potential emission reductions of up to 10%. These gains are achieved not only by optimizing single signals but also by coordinating multiple intersections to enable smoother travel across longer routes.

In cities like Kolkata, the program has helped reduce bottlenecks and improve flow in high-traffic areas. In Manchester, the system uncovered new opportunities to fine-tune signals that had previously gone unnoticed. In Boston, over 10% of the city’s signalized intersections now benefit from Green Light’s recommendations, contributing to improved travel times in one of the U.S.’s most traffic-congested cities.

The program also provides each city with a detailed impact report showing key metrics—such as reductions in driver stops and emissions—helping decision-makers expand optimization efforts with confidence.

Also read: Building the Next Silicon Valley: Where Innovation Hubs Are Blooming Globally

A Scalable Model for Urban Sustainability

Project Green Light demonstrates how AI and existing data ecosystems can be harnessed for fast, affordable sustainability solutions. By eliminating the need for new infrastructure and focusing on intelligent coordination, cities can adopt impactful emissions-reduction strategies with minimal disruption.

As road transportation continues to be a significant contributor to global greenhouse gas emissions, intelligent traffic management is no longer just about convenience—it’s becoming a key part of the climate action toolkit.