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Traffic jams have become an important part of every driver's day.
Especially big cities.
Now, researchers say they have funded a better way to control the lights.
The new system can greatly reduce the time when drivers are trapped.
And carbon emissions caused by red lights.
What we are doing is developing algorithms that allow the main transport agencies to use high
The Massachusetts Institute of Technology, which leads the study, says Carolina Osorio, a traffic-solving model that addresses optimization problems.
In two new research papers, her team described a method of combining vehicles
Less accurate horizontal data
But more comprehensivecity-
Level data on traffic patterns to generate better information than the current system provides.
Researchers say they typically optimize travel time along selected major arteries, but are not mature enough to take into account complex interactions between all streets of the city.
In addition, the current model does not evaluate the combination of vehicles on the road at a given time
As a result, they cannot predict how changes in traffic flow will affect overall fuel use and emissions.
For their test case, Osorio and co-author Kanchana Nanduri used a traffic simulation in Lausanne, Switzerland, to simulate the behavior of thousands of vehicles per day, each of which
The model even explains how driving behavior can change every day: for example, a change in signal patterns slows down a given route, which can cause people to choose alternative routes in the next few days.
For such complex models, we have been lacking algorithms to show how to use them to decide how to change the mode of traffic lights, says Osorio.
"We came up with a solution that could improve travel time throughout the city.
Dalam kasus Lausanne, ini membutuhkan pemodelan 17 persimpangan utama dan 12.000 kendaraan.
Selain mengoptimalkan waktu perjalanan, model baru ini juga berisi informasi spesifik tentang konsumsi bahan bakar dan emisi kendaraan, mulai dari sepeda motor hingga bus, yang mencerminkan pencampuran aktual dalam lalu lintas perkotaan.
Osorio mengatakan bahwa data tersebut perlu sangat detail, tidak hanya tentang data keseluruhan tim, tetapi juga data tim pada waktu tertentu.
"Berdasarkan informasi rinci ini, kita dapat mengembangkan rencana transportasi untuk meningkatkan efisiensi dalam skala kota secara praktis bagi lembaga-lembaga perkotaan."
Saat ini, tim sedang mengerjakan proyek di Manhattan dan tempat lain untuk menguji potensi sistem tersebut bagi bisnis besar.
Kontrol sinyal skala.
Di masa depan, selain mengatur waktu lampu lalu lintas, simulasi ini juga dapat digunakan untuk mengoptimalkan keputusan perencanaan lainnya, seperti memilih lokasi terbaik untuk mobil atau sepeda.
Pusat berbagi, kata Osorio.