Russian developers have achieved a technological breakthrough, applying artificial intelligence for the first time in global practice to comprehensively optimize the planning of public transport operations. This is a unique development that combines three key parameters into a single model: service schedules, vehicle dispatch to routes, and driver shift schedules. Previously, such a task was considered nearly impossible due to the high complexity of the computations.
Experiment in Moscow: figures and facts
Pilot testing of the new platform called "Opturan" was conducted at the State Unitary Enterprise "Mosgortrans" with the participation of the State Public Institution "Transport Organizer." Over two weeks, the algorithms were tested on nine routes at the "Andropova" site. The results are impressive: inter-route idle time was reduced by 34%, and the nighttime workload on drivers decreased significantly. Moreover, the optimization freed up two buses, which were redirected to reinforce other routes.
The platform was created by the Federal State Unitary Enterprise "ZashchitaInfoTrans," under the Ministry of Transport. This is not just an experiment for the sake of statistics—the technology has already demonstrated its practical value and readiness for scaling.
AI penetrates the economy: from transport to construction
The Moscow experiment is just part of a large-scale push to integrate artificial intelligence into the public sector. Earlier, President Vladimir Putin instructed the use of AI for demand forecasting and optimizing public transport schedules, calling such tools a way to radically improve the industry's efficiency. Now, that instruction has taken concrete shape.
Similar processes are underway in other areas. The Russian Ministry of Construction plans to transfer half of its processes in construction and housing and utilities to AI by 2030. The Ministry of Digital Development, together with the Ministry of Industry and Trade, is preparing standards for integrating AI agents into industrial software to avoid creating closed ecosystems. Authorities are also discussing requirements for localizing AI chips, aiming to reduce import dependence and build domestic computing capacity. Without this, scaling solutions like the Moscow transport model across the entire country will be extremely difficult.
Neighboring countries are not lagging either: in Kazakhstan, two ministries have already launched an AI service that independently analyzes legal norms and reduces the burden on businesses.
My view as an analyst: The 34% reduction in idle time is not just optimization—it is a signal that AI can tackle tasks that previously seemed unsolvable for humans. However, the key challenge is not algorithms but infrastructure. Without domestic chips and computing power, Russia risks remaining at the level of pilot projects, while the global market is already moving toward full-scale automation. This experiment is an important step, but only the first on a long journey.