Formation of an automated traffic capacity calculation system of rail networks for freight flows of mining and smelting enterprises
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- Category: Information technologies, systems analysis and administration
- Last Updated on 23 June 2016
- Published on 23 June 2016
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Authors:
S.V.Panchenko, Dr. Sci. (Tech.), Professor, Ukrainian State University of Railway Transport, Kharkiv, Ukrainе, е-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Т.V.Butko, Dr. Sci. (Tech.), Professor, Ukrainian State University of Railway Transport, Kharkiv, Ukrainе, е-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
А.V.Prokhorchenko, Cand. Sci. (Tech.), Associate Professor, Ukrainian State University of Railway Transport, Kharkiv, Ukrainе, е-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
L.O.Parkhomenko, Cand. Sci. (Tech.), Ukrainian State University of Railway Transport, Kharkiv, Ukrainе, е-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract:
Purpose. The article deals with a higher accuracy estimation of traffic capacity of rail networks intended for transportation of raw materials and finished products of mining and smelting industries, which is based on the automated analysis.
Methodology. The methods of comparative analysis, mathematic modelling and forecasting have been employed.
Findings. The article deals with the development of the method that allows taking into consideration the operating reliability of a transportation system while forming an automated traffic capacity calculation system for rail networks. The authors have proposed statistical evaluation of operating reliability of a section using simulation modelling of primary and secondary train delays in the section timetable. The instantaneous availability of the system has been proposed as an evaluation ratio of operating reliability of the section. The method established the foundation for consecutive automated traffic capacity calculation of a rail network for freight transportation of mining and smelting industries. The results of failure modelling on a rail section testify considerable influence of secondary delays on operating reliability of the scheduling technology. The research substantiates the importance of taking into account organizational and technological failures in a timetable while formalizing the traffic capacity calculation of rail networks. The dependencies of instantaneous availability on the number of trains and an accepted reliability level on the test rail section have been defined.
Originality. An automated traffic capacity calculation method for more accurate evaluation of rational workload limits of a rail network has been designed. This automated method, unlike existing ones, takes into consideration the operating reliability of the rail transportation system of mining and smelting industries.
Practical value. The proposed automated traffic capacity calculation system for rail networks makes it possible to increase accuracy of determining the maximum amount of trains on the section and avoid its overloading which will increase the speed of freight flows and influence the efficiency of transport logistics for raw materials and finished products of mining and smelting industries.
Список літератури / References:
1. Butko, T. and Prokhorchenko, A., 2015. Analysis of the research in problems of management of railway infrastructure capacity. Zaliznychnyi Transport Ukrainy, Issue 5, pp. 18–24.
Бутько Т.В. Аналіз наукових досліджень в області проблеми управління пропускною спроможністю залізничної інфраструктури / Т.В. Бутько, А.В. Прохорченко // Залізничний транспорт України. – 2015. – Вип. 5. – С. 18−24.
2. Osminin, A.T., Anisimov, V.A., Klyuev, N.A., Osminin, L.A. and Anisimov, V.V., 2012. On the automation of train schedule. Zheleznodorozhnyy Transport, Issue 4, pp. 3–9.
Об автоматизации графика движения поездов / А.Т. Осьминин, В.А. Анисимов, Н.А. Клюев [и др.] – М.: «Железнодорожный транспорт», 2012.– Вып.4. – С. 3−9.
3. Shish, V.O., Yanovskiy, P.O. and Shulgina, O.L., 2007. Automation of calculation capacity of railway lines. Zaliznychnyi Transport Ukrainy, Issue 5, pp. 56–60.
4. Branishtov, S.A., Shirvanyan, A.M. and Tumchenok, D.A., 2014. Railway Capacity Estimation Methods. Part II. Parametric Models, Optimization, Simulation. Informatsionno-Upravlyayushchiye Sistemy, Issue 6, pp. 68–74.
Браништов С.А. Методы оценки пропускной способности железных дорог. Часть 2: Параметрические модели, оптимизация, моделирование / С.А. Браништов, А.М. Ширванян, Д.А. Тумченок // Информационно-управляющие системы. – 2014. – Вып. 6. – С. 68−74.
5. Vergun, O.F., Lipovets, N.V. and BogolIy, V.M., 2002. Instruktsiya z rozrakhunku nayavnoi propusknoi spromozhnosti zaliznyts Ukrayiny [Instructions for calculating available capacity of railways of Ukraine]. Kyiv: Transport Ukrainy.
Інструкція з розрахунку наявної пропускної спромо-жності залізниць України ЦД-0036, затвердженої наказом Укрзалізниці від 14 березня 2001 р. № 143/Ц / Вергун О.Ф., Липовець Н.В., Боголій В.М. –К.: Транспорт України, 2002. – 376 с.
6. Abril, M., Barber, F., Ingolotti, L., Salido, M. A., Tormos, P. and Lova, A., 2008. An assessment of railway capacity. Transportation Research Part, vol. 44, no. 5, pp. 774–806.
7. Gruntov, P.S., 1996. Ekspluatatsionnaya nadezhnost stantsiy [Operational reliability of stations]. Moscow: Transport.
Грунтов П.С. Эксплуатационная надежность станций / Грунтов П.С. – М.: Транспорт, 1996. – 247 с.
8. Venttsel, E.S., 1999. Teoriya veroyatnostey [Probability theory], 6th edition. Moscow: Vysshaya Shkola.
Вентцель Е.С. Теория вероятностей; 6-е изд. / Вентцель Е.С. – М.: Высш. шк., 1999. – 576 c.
9. Krüger, N.A., Vierth, I. and Roudsari, F.F., 2013. Spatial, Temporal and Size Distribution of Freight Train. Delays: Evidence from Sweden. Working papers in Transport Economics, no. 8, pp. 32.
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