Russian developers have unveiled a technology that, for the first time in global practice, effectively tackles one of the most complex logistical challenges—comprehensive optimization of public transport scheduling using artificial intelligence. The results of the pilot project are impressive: inter-route idle time has been reduced by 34%.
At the core of the development lies the "Opturan" platform, created by the Federal State Unitary Enterprise "ZashchitaInfoTrans," which is under the Ministry of Transport. The key innovation is that, for the first time, algorithms have managed to combine three critical factors into a single model: the movement schedule, the timetable for vehicles entering the line, and driver shifts. Previously, such a task was considered nearly impossible due to its high computational complexity.
What the experiment showed
The algorithms were tested in Moscow on nine routes at the "Andropova" site over two weeks. The results exceeded expectations: in addition to reducing inter-route idle time by 34%, nighttime workload on drivers was also lowered. Moreover, the optimization freed up two buses, which were redirected to strengthen other, busier routes.
This success is a natural step in the state's systematic efforts to integrate AI into key industries. Public transport, with its complex, multi-factor logistics, proved to be an ideal platform for demonstrating the capabilities of algorithms that process data arrays at speeds unattainable for humans. The tasks of allocating transport demand, creating schedules, and planning routes fit perfectly on AI's shoulders, which sees patterns where the human eye sees only chaos.
AI penetrates all sectors of the economy
The transport initiative is just one part of a large-scale trend. Earlier, President Vladimir Putin instructed the use of AI for demand forecasting and schedule optimization, and the Moscow experiment turns this directive into concrete practice. Similar processes are underway in other sectors: the Ministry of Construction aims to transfer half of processes in construction and housing and utilities to AI by 2030, while the Ministry of Digital Development, together with the Ministry of Industry and Trade, is preparing standards for integrating AI agents into industrial software.
The authorities are also building infrastructure for these projects. Requirements for localizing AI chips are being discussed to reduce dependence on imports. Without domestic computing power, scaling solutions like the Moscow transport model across the entire country will be extremely difficult. Neighbors are not lagging behind either: in Kazakhstan, two ministries have already launched an AI service that independently analyzes legal norms, reducing the burden on businesses.
My view: Reducing idle time by a third is not just optimization—it is a paradigm shift in urban infrastructure management. Such algorithms are the foundation for future "smart cities," where data and AI become as fundamental resources as electricity or water. Investors and developers should closely watch this direction: it is here that standards are being formed, which will then be replicated in other countries and markets.