According to the communication, this program analyzes CCTV images, recognizes different vehicles and tracks their speed and trajectory with an accuracy of up to 30 centimeters, making it possible to form visual maps of traffic flow difficulties in real time.

Olga Ivanova, associate professor at the university’s Department of Systems Programming, said that the program’s peculiarity is that it can detect even small deviations in traffic, such as a reduction in lane width.

The system signals possible obstacles, such as traffic accidents or repairs.

The intersection display is updated every two seconds, with a color-coded indication of congestion: the more cars in an area, the redder the box.

In the future, the neural network will not only be able to detect anomalies, but also classify them by type, predicting possible traffic jams and their impact on traffic in the next 10-20 minutes.

This will help the relevant departments and services to respond promptly to road problems and prevent the situation from worsening. Ivanova says the development has an important advantage: it is easy to integrate into the infrastructure of any city and does not require the installation of expensive GPS sensors in each vehicle.

The project is part of the intelligent transport control system AIMS ECO ROAD, which is already in operation in several large Russian cities.

The introduction of the new program is expected to improve the transportation situation in cities by increasing traffic speed and reducing the environmental burden.

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