Whilst it’s a shame this implementation sucks, I wish we would get intelligent traffic light controls that worked. Sitting at a light for 90 seconds in the dead of night without a car in sight is frustrating.
That was a solved problem 20 years ago lol. We made working systems for this in our lab at Uni, it was one of our course group projects. It used combinations of sensors and microcontrollers.
It’s not really the kind of problem that requires AI. You can do it with AI and image recognition or live traffic data but that’s more fitting for complex tasks like adjusting the entire grid live based on traffic conditions. It’s massively overkill for dead time switches.
Even for grid optimization you shouldn’t jump into AI head first. It’s much better long term to analyze the underlying causes of grid congestion and come up with holistic solutions that address those problems, which often translate into low-tech or zero-tech solutions. I’ve seen intersections massively improved by a couple of signs, some markings and a handful of plastic poles.
Throwing AI at problems is sort of a “spray and pray” approach that often goes about as badly as you can expect.
To back up what you’re saying, I work with ML, and the guy next to me does ML for traffic signal controllers. He basically established the benchmark for traffic signal simulators for reinforcement learning.
Nothing works. All of the cutting edge reinforment algorithms, all the existing publications, some of which train for months, all perform worse than “fixed policy” controllers. The issue isn’t the brains of the system, its the fact that stoplights are fricken blind to what is happing.
Whilst it’s a shame this implementation sucks, I wish we would get intelligent traffic light controls that worked. Sitting at a light for 90 seconds in the dead of night without a car in sight is frustrating.
That was a solved problem 20 years ago lol. We made working systems for this in our lab at Uni, it was one of our course group projects. It used combinations of sensors and microcontrollers.
It’s not really the kind of problem that requires AI. You can do it with AI and image recognition or live traffic data but that’s more fitting for complex tasks like adjusting the entire grid live based on traffic conditions. It’s massively overkill for dead time switches.
Even for grid optimization you shouldn’t jump into AI head first. It’s much better long term to analyze the underlying causes of grid congestion and come up with holistic solutions that address those problems, which often translate into low-tech or zero-tech solutions. I’ve seen intersections massively improved by a couple of signs, some markings and a handful of plastic poles.
Throwing AI at problems is sort of a “spray and pray” approach that often goes about as badly as you can expect.
(I know I’m two months late)
To back up what you’re saying, I work with ML, and the guy next to me does ML for traffic signal controllers. He basically established the benchmark for traffic signal simulators for reinforcement learning.
Nothing works. All of the cutting edge reinforment algorithms, all the existing publications, some of which train for months, all perform worse than “fixed policy” controllers. The issue isn’t the brains of the system, its the fact that stoplights are fricken blind to what is happing.