This article examines methods for enhancing the capacity utilization efficiency of long-distance passenger trains operating under state-regulated tariffs. Unlike traditional revenue management systems based on dynamic pricing, socially significant passenger rail services are constrained by fixed tariffs and cannot employ price mechanisms to regulate demand. Under these conditions, intelligent management of seat allocation across various Origin–Destination (O&D) city pairs becomes the primary instrument for increasing operational efficiency. We propose a comprehensive capacity management methodology based on train classification by occupancy level, route segmentation by contribution to passenger turnover, application of Virtual Nesting, unconstrained demand estimation, and Route Splicing technology. Control algorithms were developed for five train categories based on predicted load factors. An empirical analysis using real-world operational data from JSC "Passenger Transportation" (Kazakhstan) for the 2022–2025 period, combined with simulation modeling, demonstrates a potential 6–12% increase in passenger turnover compared to the baseline static bucket allocation method. The findings show that maximizing passenger turnover can improve the efficiency of subsidized rail operations without altering existing tariff policies.