The RailRoad program, developed by Roman Machanov and implemented on the Privolzhskaya Railway in 2018, addresses one of the most pressing challenges in railroad logistics—the lack of coordination between stations, locomotives, and crews. The system collects real-time data through API integration with the Automated Control System, infrastructure sensors, and dispatcher logs, then processes this information using a mathematical model based on linear programming methods. The algorithms utilize historical data—including regression analysis and moving averages—to forecast bottlenecks and suggest adjustments to train formation plans.
A key observation that served as the foundation for development is the cyclical nature of the railway network’s operations, with a cycle of approximately four hours. This rhythm is determined by the shift schedules of locomotive crews, technological intervals at sorting stations, and the preparation time for trains. Instead of planning operations linearly, the system adapts to this cycle, preventing situations where a ready train cannot depart due to a lack of traction or where a locomotive is idle without assignment.
RailRoad is integrated with the company’s ERP platform. This allows for consideration of not only the technical condition of rolling stock but also labor standards, crew qualifications, and maintenance schedules. The program automatically coordinates interactions between sorting stations, reducing delays in the transfer of train flows. In practice, this has led to a 23% reduction in locomotive downtime and a 15% increase in network capacity in the areas of implementation.
Roman Machanov is a specialist in crisis management and digital automation of operational processes. Throughout his career, he has implemented over 90 projects in energy, industry, and logistics. His approach is based on identifying hidden patterns within complex systems and creating solutions that eliminate imbalances rather than masking them. He is active in professional communities, including the IT Directors Club “4CIO,” the industry association “NeuroNet,” and the Russian Geographical Society. For his contributions to the development of Moscow’s digital infrastructure, he received a letter of appreciation from the city’s government.
Since 2020, he has been sharing his experience through educational activities, teaching a course on “Identifying Inefficiencies and Developing Automated Systems to Eliminate Them in Enterprises.” In this course, he demonstrates how systematic analysis of operational data can uncover reserves that are not visible through traditional management practices. An example involving railway logistics is one of the key case studies in this course.
The context of the development is the increase in freight traffic on Russian railways. According to the Ministry of Transport, in 2020, the volume of transportation exceeded 1.2 billion tons, and further increases without improved management efficiency threatened to overload the infrastructure. This issue was particularly acute at junction stations and the borders of dispatching sectors, where the lack of a unified coordination system led to delays and equipment downtime.
Today, the RailRoad system has already become a part of the daily operations of dispatchers on the Privolzhskaya Railway. This is not a pilot project or a concept — it is a working tool integrated into traffic management processes. It does not replace humans but assists dispatchers in making faster decisions, reducing cognitive load, and improving planning accuracy. In the face of growing logistics demands, such solutions are becoming not just an addition but a necessary element of modern infrastructure.
May 12, 2021
