It's neat to apply machine learning to this.

Video traffic detection has been around for at least a decade, using cameras to detect cars in lanes and count congestion. They were very smart at detecting and counting vehicles (and pedestrians in crosswalks), well before AI became hyped. They are just massively expensive and have a ton more infrastructure and maintenance requirements, than old-reliable magnetic detectors. How often will your traffic crew need to clean or replace cameras, blocking traffic from a bucket truck, due to spiders making homes on the lenses, dust/dirt, sun or ocean saltwater damage to lenses, or snow?

The simple reality, is that magnetic loop detectors are used because they are cheap. Multiple ones can be added with minor cost and know congestion levels instantly, well before the vehicles get to the light. Yet, larger, more congested urban cities frequently need to surpass this to use video detection anyways, despite its pitfalls.

This product will need to grow to manage entire corridors of traffic lights or whole congested portions of cities to be very useful. The type of busy intersection which could afford this fancy of attention, would be heavily dependent on coordination; the intersection lights down the road all need to be coordinated in sync with this one.

It sounds like this idea could probably be expanded to do just that. If it can manage multiple lights in a congested down-town portion of a city, while keeping them in coordination, they'd have something pretty solid. If it could also do so automatically, without any light phase timing manually configured, it would be massive. Typically they have engineers do surveys of each intersection, work up complex formulas, and configure the proper phase timing routines in each traffic controller, adhering to required times for pedestrian to cross, yellow lights (in the US, or comparable), and other safety requirements. This constitutes a huge setup cost and complexity with coordination.