Russian developers have achieved a global breakthrough by applying artificial intelligence for the first time to comprehensively optimize public transport scheduling. This involves creating a unique technology that combines three key parameters into a single model: timetables, vehicle deployment on routes, and driver shift schedules. Previously, such a task was considered nearly impossible due to the immense computational complexity.
Experiment in Moscow: Impressive Results
The new technology, developed on the "Opturan" platform (created by the subordinate federal state unitary enterprise "ZashchitaInfoTrans"), underwent two weeks of trials on nine bus routes at the "Andropova" depot of the state unitary enterprise "Mosgortrans." The results exceeded all expectations: inter-route idle times were reduced by an impressive 34%, and the nighttime workload on drivers significantly decreased. Moreover, the optimization freed up two buses, which were redirected to strengthen other, busier routes.
This is not just a laboratory experiment but a real, working system. Integrating AI into urban transport logistics is a logical step within the state's digitalization policy. The transport sector, with its complex logistics and numerous variables, proved to be an ideal testing ground for algorithms capable of processing vast amounts of data faster and more efficiently than humans.
AI Everywhere: From Construction to Industry
The success of the Moscow experiment is just the tip of the iceberg. Authorities are actively implementing artificial intelligence across various economic sectors. By 2030, the Ministry of Construction plans to transfer half of processes in construction and housing and utilities to AI, while the Ministry of Industry and Trade, together with the Ministry of Digital Development, is developing standards for integrating AI agents into industrial software. Simultaneously, requirements for localizing AI chips are being discussed to reduce import dependence and create a domestic computing base. Without this, scaling solutions like the Moscow transport model nationwide would be extremely challenging.
Similar processes are underway among neighbors: in Kazakhstan, an AI service has already been launched that independently analyzes legal norms and reduces the burden on businesses. The world is entering an era where algorithms take on more routine and complex tasks.
My view: Reducing idle times by 34% is not just optimization but a paradigm shift in management. It proves that AI can solve tasks deemed impossible for classical mathematical models. It is critically important that this is followed by building domestic AI infrastructure; otherwise, dependence on foreign technologies will become a new bottleneck for the entire economy.