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Building on our decades-long effort to map cities across the world, we can infer existing traffic light parameters including: cycle length, transition time, green split (i.e. right-of-way time and order), coordination and sensor operation (actuation).

Green Light uses AI and Google Maps driving trends, with one of the strongest understandings of global road networks, to model traffic patterns and build intelligent recommendations for city traffic engineers to optimize traffic flow. Early numbers indicate a potential for up to 30% reduction in stops and 10% reduction in greenhouse gas emissions (1). By optimizing each intersection, and coordinating between adjacent intersections, we can create waves of green lights and help cities further reduce stop-and-go traffic. Green Light is now live in over 70 intersections in 13 cities, 4 continents, from Haifa, Israel to Bangalore, India to Hamburg, Germany – and in these intersections we are able to save fuel and lower emissions for up to 30M car rides monthly.Â

Relight

No, all recommendations are based on driving trends from Google Maps and are then implemented by the city using the city’s existing systems and equipment.

Green Light algorithms will continue to monitor all relevant metrics, and two weeks after implementation a full impact analysis report will be uploaded to the interface for city engineers to review.

Translational Relevance: We demonstrated that PS-OCT has the potential to evaluate the status of RNFL structural damage in eyes with severe glaucoma, which is currently challenging in clinics.

We measure how many stops we’ve saved for drivers, and its impact on traffic patterns. We then use industry standards models to calculate the climate impact of these changes. We share this with the partner city and continue monitoring for any future needed changes.

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Green Light reflects Google Research's commitment to use AI to address climate change and improve millions of lives in cities around the world.

We create a model to understand how traffic flows through the intersection. This helps us understand typical traffic patterns including patterns of starting and stopping, average wait times at a traffic light, coordination between adjacent intersections (or lack thereof), and how traffic light plans change throughout the day.

Conclusions: RR and RNFLT have similar performance in glaucoma diagnosis. However, in patients with glaucoma especially severe glaucoma, RR showed a stronger correlation with VF test results. Further research is needed to validate RR as an indicator for severe glaucoma evaluation and to explore the benefits of using PS-OCT in clinical practice.

Using AI, we identify possible adjustments to traffic light timing. We share these adjustments as actionable recommendations with the city. The city’s traffic engineers review the recommendations, approve them, and they can easily implement them in as little as 5 minutes, using the city's existing policies and tools.

LightOn

Road transportation is responsible for a significant amount of global and urban greenhouse gas emissions. It is especially problematic at city intersections where pollution can be 29 times higher than on open roads.  At intersections, half of these emissions come from traffic accelerating after stopping. While some amount of stop-and-go traffic is unavoidable, part of it is preventable through the optimization of traffic light timing configurations. To improve traffic light timing, cities need to either install costly hardware or run manual vehicle counts; both of these solutions are expensive and don’t provide all the necessary information.

The Green Light dashboard provides city-specific actionable recommendations, showing supporting trends for each recommendation, with the option to accept or reject the suggestion. After a recommendation has been implemented, the dashboard shows an impact analysis report.

Green Light only shares recommendations about how a city should optimize traffic light timing - for example if they should add additional seconds of “green time” to a particular part of the traffic light cycle. User data is never shared with the city or any other third party.Â

When we start working with a city, the Green Light algorithm investigates driving patterns through the city, uses insights from Google Maps, and provides recommendations for intersections to optimize, based on the expected impact of the optimization. For example, if a traffic light at a certain intersection is already on the best possible plan, the system would not provide a recommendation for it.

IC-LightAI

Methods: This cross-sectional pilot study analyzed 170 eyes from 49 healthy individuals and 68 patients with glaucoma. The patients underwent PS-OCT imaging and conventional spectral-domain optical coherence tomography (SD-OCT), as well as visual field (VF) tests. Parameters including RR and retinal nerve fiber layer thickness (RNFLT) were extracted from identical circumpapillary regions of the fundus. Glaucomatous eyes were categorized into early, moderate, or severe stages based on VF mean deviation (MD). The diagnostic performance of RR and RNFLT in discriminating glaucoma from controls was assessed using receiver operating characteristic (ROC) curves. Correlations among VF-MD, RR, and RNFLT were evaluated and compared within different groups of disease severity.

LyteAI

We are currently in the early research phase and offering Green Light to partner cities at no cost. Our primary goal of this product is to support cities’ sustainability goals.

Results: The diagnostic performance of both RR and RNFLT was comparable for glaucoma detection (RR AUC = 0.98, RNFLT AUC = 0.97; P = 0.553). RR showed better structure–function association with VF-MD than RNFLT (RR VF-MD = 0.68, RNFLT VF-MD = 0.58; z = 1.99; P = 0.047) in glaucoma cases, especially in severe glaucoma, where the correlation between VF-MD and RR (r = 0.73) was significantly stronger than with RNFLT (r = 0.43, z = 1.96, P = 0.050). In eyes with early and moderate glaucoma, the structure–function association was similar when using RNFLT and RR.

Once a city signs an agreement with Green Light, they get access to our interface, where city officials can view suggested recommendations, supporting information, and monitor their measured impact on emissions and traffic flow.

Reshma Radhakrishnan Parakkel, Damon Wong, Chi Li, Jocelyn Cheong, Monisha Esther Nongpiur, Rachel Shujuan Chong, Tin Aung, Leopold Schmetterer, Xinyu Liu, Jacqueline Chua; Retinal Nerve Fiber Layer Damage Assessment in Glaucomatous Eyes Using Retinal Retardance Measured by Polarization-Sensitive Optical Coherence Tomography. Trans. Vis. Sci. Tech. 2024;13(5):9. https://doi.org/10.1167/tvst.13.5.9.

Purpose: To assess the diagnostic performance and structure–function association of retinal retardance (RR), a customized metric measured by a prototype polarization-sensitive optical coherence tomography (PS-OCT), across various stages of glaucoma.

No. Our system offers better plans based on aggregated anonymous data to improve traffic flow for everyone: Google and non Google users, car drivers, taxi drivers, buses and all other users of the road.