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The article is devoted to the development of a mathematical model for predicting time losses in railway container transportation. Losses are classified within the context of the lean manufacturing concepts: muda (waiting), muri (overburden), and mura (unevenness). The relationship between the magnitude of time losses, flow unevenness, and resource utilization was investigated using the mathematical tools of graph theory and queuing theory. An adapted Pollaczek-Khintchine formula was applied to calculate the average queue length and the expected container delay time. The proposed formula allows predicting the average time losses arising during container delivery, caused by the overburden of transport hubs and/or individual terminals, as well as container flow variability. Model verification was conducted based on real data from the Zabaikalsk railway border crossing point, demonstrating the adequacy of the forecast. However, improving prediction accuracy requires model modification by incorporating additional factors influencing queue formation, such as: service priority for specific flows, the non-deterministic nature of service speed, and other variables not accounted for in the current model version. Using the model, dependencies were established between a container's queue dwell time, the hub's utilization level, and the flow variation coefficient. The study showed that the impact of flow variability on the queue is more significant at high hub utilization levels. Therefore, under conditions of infrastructure shortage, it is critically important to ensure a reduction in container flow unevenness and uncertainty. Recommendations for reducing time losses were substantiated, including increasing throughput capacity, synchronizing train schedules, and distributing cargo along routes with minimal load. The conclusions have practical significance for container market participants, enabling them to predict delivery times and choose routes with minimal logistical risks and losses. The results can contribute to enhancing the overall operational efficiency of Russian railways.