It's late at night and your city gets an empty intersection waiting for the red light to turn green.
You begin to debate the moral ramifications of just driving through the red light,
breaking the societal norms that separate human from beast when the thought occurs to you.
Why is this light even holding you up in the first place?
The answer lies in the large number of ways that traffic lights decide whether to turn red or green.
In lightly traveled areas, some of them are on simple timers, a technology that dates back to 1922,
especially during the wee hours when very few people are on the road, kind of like in 1922,
which explains why you're sometimes stuck at a red for no apparent reason.
But fortunately, most lights are far smarter than that.
Probably the most common way that lights try to react to traffic conditions in some sensible way
is through the use of an induction loop. You've probably seen these when you pull up to an intersection
and you can see what looks like an outline in the pavement near the stop line.
The loop is really nothing more than a piece of wire with a current running through it.
When a car drives over it, the metal in the car's body decreases the impedance of the wire.
A sensor detects this and lets an electronic controller know that a car is waiting,
meaning that the light will turn green before long. Unless, like me, you drive a motorcycle and you miss the inductor.
And this doesn't do much when it comes to managing traffic over a larger area,
where heavy traffic at multiple intersections and close proximity
requires coordination with each other to ensure that the streets don't become a snarled mess.
Sometimes you can see a simple example of this in busy downtown areas,
where several lights in succession are timed to turn green at once,
allowing cars to flow freely for several blocks, but then also timed to stop you at some point to disincentivize speeding.
Now, unsurprisingly, large cities often have some kind of central computer network
to monitor and coordinate traffic lights. One great example of this is Sydney, Australia, which uses a system called SCAPS
to route over 100 million vehicles every day.
Their system works by gathering real-time traffic information,
which is fed into those controller boxes that you might have seen at street corners.
Although much of the system relies on the aforementioned induction loops,
the controller can measure the gaps between vehicles
and use that information to determine when it's time to change the lights.
But all that data gets uploaded to a regional server, which applies algorithms to that data to control large numbers of intersections over a wide area.
In this way, the system can adapt to real-time conditions,
improving traffic flow compared to a series of individually controlled lights.
In Sydney, the government of New South Wales claims that SCAPS has resulted in a 28% overall reduction in travel time,
a 25% reduction in stops, and a 12% reduction in fuel consumption.
Not bad considering that the city is home to over 5 million people.
SCAPS has been deployed in many other cities worldwide as well, but unsurprisingly, much of the future in smart cities
may instead revolve around predictive models that use machine learning to anticipate problems.
We've already seen one version of this installed in Pittsburgh,
where a system called SurTrack allows intersections to communicate with each other
to generate a signaling model based on current traffic conditions.
And of course, work is being done on training traffic lights with machine learning.
At Aston University in Birmingham, England, a recent study that used a neural network for reacting to simulated traffic via cameras
showed that an AI model outperformed other current real-world methods of traffic management.
And AI will likely become even more important once we have truly self-driving cars
that will need to communicate with traffic signals to avoid clogs
or, well, other undesirable outcomes at intersections.
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