Premises of the creation of a digital traffic safety management system

Aim.The digital transformation of the traffic safety management system in JSC RZD involves top-level integration with the operating processes of all business units in terms of integral assessment of the risk of possible events and achievement of specified indicators. The result will be the merger of the traffic safety management system with the processes of all levels of the company’s management enabled by an integrated intelligent system for managing processes and services whose functionality includes real-time traffic safety management.

Methods. The paper uses system analysis of existing approaches and methods of processing of large quantities of structured and unstructered data.

Results. The paper examines the development stages of train traffic safety management, as well as automated information and control systems that enable traffic safety management. General trends in the creation of systems for collection and processing of information are analyzed. The applicability of such technologies as Big Data, Data Mining, Data Science as part of advanced control systems is shown. The paper examines the performance of the above technologies by analyzing the effect of various factors on the average daily performance of a locomotive, where, at the first level, such factors as average daily run of a locomotive, average trainload are taken into consideration; at the second level, the focus is on the service speed, locomotive turnover at station, etc.; at the sixth level, the focus is on the type of locomotive, its technical state, etc. It is shown that statistical methods of factor analysis and link analysis combined with such other methods of Data Mining as methods of simulation and prediction, the average daily performance of a locomotive can be planned proactively. The author proposes a procedure of migration towards a digital traffic safety management system that would be based on models of interaction of safety and dependability factors of all railway facilities at all railway levels of hierarchy, as well as in association with other factors that have no direct relation to dependability, yet affect the safety of the transportation process.

Conclusions. The primary benefit of migration towards Big Data consists in the development of a dynamic model of traffic safety, the elimination of human factor in control systems. Most importantly, it enables the creation within the Russian Railways company (JSC RZD) of an integrated intelligent process and service management system that enables real-time traffic safety management. An extensive process of development and deployment within the company of the URRAN Single Corporate Platform (SCP) enabled executive decision support as regards risk-based functional dependability and safety of transportation facilities. Thus, the URRAN SCP sets the stage for the digital transformation of the traffic safety management system in JSC RZD.

1. Rozenberg IN, Avetikian MA, Zamyshlyaev AM. Avtomatizirovannaya informatsionnaya sistema revizora dvizheniya [Traffic supervisor’s automated information management system]. Zheleznodorozhny transport 2004;7:46-48 [in Russian].

2. Zamyshliaev AM. Prikladnye informatsionnye sistemy upravleniya nadezhnostyu, bezopasnostyu, riskami i resursami na zheleznodorozhnom transporte [Applied information systems for management of dependability, safety, risks and resources in railway transportation]. Moscow: Nadiozhnost; 2013 [in Russian].

3. Rozenberg EN, Rozenberg IN, Zamyshliaev AM et al. Sistema KASANT: zadachi, vozmozhnosti, perspektivy razvitiya [KASANT system: tasks, capabilities, prospects of development]. Zheleznodorozhny transport 2008;9:6-9 [in Russian].

4. Zamyshliaev AM, Proshin GB, Gorelik AA. Sistema KASANT: vtoroy etap vnedreniya [KASANT system: the second stage of deployment]. Avtomatika, sviaz, informatika 2009;7:9-13 [in Russian].

5. Gapanovich VA, Shubinsky IB, Zamyshlyaev AM. Mathematical and information support of the URRAN system. Dependability 2012;3:12-19.

6. Gapanovich VA, Shubinsky IB, Zamyshliaev AM. Risk assessment of a system with diverse elements. Dependability 2016;16(2):49-53.

7. Rizzatti R. Digital Data Storage is Undergoing Mind-Boggling Growth. https://www.eetimes.com/author.asp?section_id=36&doc_id=1330462>, [accessed 31.10.2018].

8. Makevnin B, Stoliarov A. Tsifrovaya neft. Bolshie dannye kak odin iz klyuchevykh instrumentov tsifrovoy transformatsii [Digital oil. Big data as one of the key tools of digital transformation]. Sibirskaya neft 2017;9(146):10-15.

9. Thaduri A, Galar D, Kumar U. Railway assets: A potential domain for big data analytics. Lulea (Sweden): Lulea University of Technology. Procedia Computer Science 2015;53:457‑467.

10. Gapanovich VA, Shubinsky IB, Zamyshliaev AM. Postroenie i ispolzovanie matrits riskov v sisteme upravleniya riskami na zheleznodorozhnom transporte [Design and application of risk matrices as part of risk management systems in railway transportation]. Dependability 2011;4:56-68 [in Russian].

11. Zamyshlyaev A, Shubinsky I. Adaptive Management System of Dependability and Safety of Railway Infrastructure. Second International Symposium on Stochastic Models in Reliability Engineering, Life Science and Operations Management (SMRLO). Be’er-Sheva (Israel): IEEE Xplore Digital Library; 2016. p. 244‑250.

12. Shubinsky I, Zamyshlyaev A. Risk management system on the Railway Transport. Second International Symposium on Stochastic Models in Reliability Engineering, Life Science and Operations Management (SMRLO). Be’er-Sheva (Israel): IEEE Xplore Digital Library; 2016. p. 481-486.

13. Shubinsky IB, Zamyshlyaev AM. Osnovnye nauchnye i prakticheskie rezultaty razrabotki sistemy URRAN [Primary scientific findings and practical effects of the URRAN system development]. Dependability 2012;3:312 [in Russian].