Forecasting rolling stock indicators in the market of transport services using the neural network method

Autor: Zadorozhniy Vyacheslav, Zyryankina Ksenia, Bakalov Maksim
Jazyk: English<br />French
Rok vydání: 2024
Předmět:
Zdroj: E3S Web of Conferences, Vol 549, p 04007 (2024)
Druh dokumentu: article
ISSN: 2267-1242
DOI: 10.1051/e3sconf/202454904007
Popis: The research is aimed at identifying patterns in the formation of tariffs, forecasting wagon rental rates and other indicators of the use of rolling stock using the neural network method. The process of tariff formation in the transport services market is investigated using the neural network method on the 1C platform. Various indicators of wagon use that affect the wagon rental rate are presented. They consider various approaches to tariff setting and propose principles for an optimal approach to setting tariffs in modern conditions. The results of the research can be applied in the further development of methods for forecasting and setting tariffs in railway transport. Keywords. railway tariff, wagon, rolling stock indicators, mathematical analysis, neural network method, forecasting.
Databáze: Directory of Open Access Journals