Hi, this is Wayne again with a topic “Here’s Why You Get Stuck At Red Lights”.
It’S late at night and you’re sitting at 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 missed the inductor, and this doesn’t do much when it comes to managing traffic over a larger area, where heavy traffic at multiple intersections in 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, but You’Re here to learn about the really high-tech tools of the present and the future that have to think about millions of vehicles at once. Right we’ll tell you more right after we think covalence for sponsoring this video covalence is an online technical trade school that provides a personalized learning experience for entry-level software developers. Unlike other programs, covalence empowers individual students with dedicated instructors, live webinars and personalized lab reviews.
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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 scats to Route over a hundred 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 scat has resulted in a 28 overall reduction in travel time, a 25 reduction in stops and a 12 percent reduction in fuel consumption, not bad. Considering that the city is home to over 5 million people, scats 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 Sir track 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 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. If you enjoyed this video hit the like button, if you didn’t well hey there’s that other button too, you can check out our other videos if you want more and don’t forget to subscribe and leave a comment below.
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