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Smart Traffic Light System Reduces Traffic Jams and Pollution, Boosts Fuel Efficiency


149631395-660x494Researchers at MIT have found a way to drastically reduce urban air pollution, traffic jams and even boost fuel economy. The solution is an algorithm for better traffic light control.

Urban pollution has been a subject of heated discussions for quite some time now, and this is the case purely because there was no single formula to solve it all. But if there was ever such thing, there is no doubt that researchers from MIT would find it. And it so happened that they did.

The scientists developed a model that simulates different situations, taking into account the behavior of drivers in traffic jams. Typically, traffic lights are controlled and synchronized only on major intersections, leaving aside the alternative routes, which often become congested as a result.

Now, the new, smart traffic light system, controlled by the algorithm developed by a team of researchers from the MIT, not only takes into consideration all complex road intersections in a city, but also adds type of vehicles, leading to accurate predictions of traffic flow.

To come up with the ultimate algorithm, the researchers used data from the roads of the busy city in Switzerland, Lausanne. Their model is based on 12,000 vehicles and 17 intersections in the city, which was sufficient for the software to establish the best travel times.

Another key advantage of this smart traffic light system is that it provides city planers with accurate predictions of pollution and emission levels, as well as estimates of the best fuel efficiency for city vehicles of various types.

This system is a real breakthrough, and it can play a crucial role in future traffic management, where more realistic scenarios will be tested before any system is implemented. This also means that the chances of success will be much higher.

The next step in front of the team is to upscale the model and test whether it is just as applicable in bigger and more congested cities.

Image (c) Getty

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