An example of the use of AI in the urban environment is waste management. Systems can monitor the amount of waste in containers and optimize the routes of garbage trucks. For example, Sensoneo offers a solution that allows you to monitor the filling of containers in real time and build waste collection routes in such a way as to minimize costs and reduce the impact on the environment.
In Moscow, neural networks search for uncleared snow, overflowing trash cans, dirty courtyards, unmown lawns, or saudi arabia phone number broken street lamps. This allows for prompt response to problems and maintaining the urban landscape in proper condition.
AI is actively used by autonomous cars. Tesla, Siemens, Yandex and other companies have developed this technology. The latter, for example, is currently testing 120 cars in Russia. Yandex also has delivery robots that bring people groceries and other orders in Moscow and St. Petersburg.
One of the most notable applications of artificial intelligence is a facial recognition system that helps find criminals and prevent crime. For example, NEC has developed one. It is actively used in a number of countries to monitor public places and improve security.
The advantage of such systems is that they allow you to instantly identify suspicious individuals in a crowd and transmit information to law enforcement agencies. This significantly increases the speed of response to potential threats and reduces the crime rate in cities. Since 2020, the Sfera video analytics system has been operating in Moscow. According to the Department of Transport, about 1,500 criminals have been identified with its help. Sfera has also helped find more than a thousand people listed as missing in transport.
In addition to what is already in use, there are many more projects that are in the development or testing stage. For example, Urban GPT, a neural network for designing urban environments, was presented at the KDD 2024 conference. According to the developers, this is an advanced spatio-temporal language model capable of predicting urban processes with high accuracy with minimal input.
Conclusion
In the era of big data, artificial intelligence is becoming an integral part of modern city life. Its application covers a wide range of aspects, from data analysis and forecasting to transport management and infrastructure design. The introduction of “smart” algorithms allows us to improve the quality of infrastructure and opens up opportunities that seemed inconceivable a couple of decades ago.
Today, an online map of Singapore provides information about what is happening on the street at this very minute — from the number of people or traffic to the flood level or a bird's eye view of the city. In Moscow, neural networks are involved in more than 70 projects: you can use Face Pay to get into the metro, chatbots help collect patient complaints, and garbage is sorted using robotic systems.
The effectiveness of using AI in the urban environment is proven by figures. According to the ANO "Digital Economy" for 2024, in Russia the use of neural networks can reduce the number of accidents by 8.2%, and the level of traffic jams - by 50%. Queues in medical institutions can be reduced by 30% and losses from downtime and repairs by up to 65%.
The prospects for urban development using neural networks are enormous. In the coming years, we can expect a further expansion of the use of AI in urban management, which will lead to even more significant changes.