Improving the methodology for determining the losses in section speed of freight trains

Abstract

   To assess the quality of operational work of railways, a number of indicators are used, one of which is the section speed of freight trains. The fulfillment of other indicators of operational work depends on this indicator. In turn, this indicator is influenced by a large number of factors, which are difficult to take into account using generally accepted analytical dependencies. This prevents the objective establishment of a planned task for section speed, as well as the assessment of losses allowed in implementation.
   Therefore, a method was proposed that allows you to estimate the losses of section speed, based on taking into account the whole variety of factors that affect it. The method is based on the presence of a statistical relationship between the section speed and the working fleet of freight cars. The size of the working fleet, in turn, is formed under the influence of all factors leading to a slowdown in the passage of car flows.
   Using the tools of probability theory and mathematical statistics, the article examines the influence of the working fleet of freight cars at individual territorial administrations of the railway on the section speed of freight trains on the railway. The purpose of this study was to increase the accuracy of determining the losses of section speed of freight trains in conditions of difficulties in passing car flows.
   In the course of the study, carried out on the statistical data of the Gorky Railway for 2022–2023, it was found that if the change in the working fleet of freight cars on the railway explained 85% of the variability of the section speed on the railway, then when switching to the working fleet of freight cars of individual territorial administrations of the railway, the degree of explanation of the variability of the final indicator increased to 90 %. Based on the results of the study, a regression equation was compiled to determine the losses of section speed, which made it possible to reduce the forecast error by 2,5 times.