Intelligent transport systems development. Vadim Shmal

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СКАЧАТЬ increased energy consumption, negative impact on the environment, etc. In addition, ITS is an incentive for the development of a number of industries and new innovative technologies. The latter include technologies for the creation of intelligent control and monitoring systems, the creation of new transport systems and their management, the production of nanomaterials, the creation of energy-saving systems for transportation, distribution and consumption of heat and electricity in the field of railway transport, processing, storage, transmission and protection of information, software production, risk reduction and reduction of the consequences of natural and man-made disasters, etc.

      A nationwide ITS program is being developed in Russia, which can become an effective tool for implementing the Transport Strategy of the Russian Federation for the period up to 2030. In particular, the Federal Law «Intelligent Transport System of the Russian Federation» is currently being discussed. In the draft of this law, the intelligent transport system is defined as an integral part of the infrastructure of the transport complex, implementing the functions of automated management, information, accounting and control to ensure the legal, financial, technological and information needs of participants in the transport process, as well as meeting the requirements of transport, information and economic security of society. As follows from this definition, it is assumed that the system integration of modern information and communication technologies and automation tools into the transport infrastructure, vehicles in order to improve the safety and efficiency of transport processes. In relation to railway transport, the development of ITS is defined by such a directive document as the Strategy for the Development of Railway Transport in the Russian Federation for the period up to 2030.

      2.4 Goals and objectives of ITS creating in railway transport

      The goals of creating intelligent railway transport systems are to reduce the transport losses of the population and transport costs in the sphere of economy, business and services, to intensify economic and social processes, to improve traffic safety, to improve the environmental situation, to reduce the negative impact of the human factor on the quality of management, to increase the attractiveness of railway transport for passengers and cargo owners. Achieving these goals involves solving a large number of tasks. These, in particular, include:

      ■ improving the efficiency of using the existing railway network by more evenly distributing railway rolling stock in time and space;

      ■ improvement of technological, informational and social components of traffic safety;

      ■ providing managers at all levels with the necessary information to make operational and strategic decisions based on modeling and assessing the impact on the transport system of new and modernized transport facilities;

      ■ formation of a rapid response scheme of transport services, which allows to quickly take measures in case of emergencies, adverse weather conditions, etc.;

      ■ creation of monitoring systems for transport infrastructure and traffic conditions, allowing to assess the state of the transport system in real time and predict its changes.

      3 MODERN SCIENTIFIC AND METHODOLOGICAL APPROACHES TO THE ITS CREATION IN RAILWAY TRANSPORT

      To date, there is no unified understanding of what intelligent transport systems are. In many publications and speeches, they are more or less identified with conventional automated transport systems. An important feature of ITS, which makes it possible to distinguish such systems into a separate class and even into a separate area of research in railway science, is the formal logical and mathematical tools used to solve problems from the standpoint of a system-wide approach to the analysis and management of all systems and processes in railway transport.

      It should be emphasized that modern railway transport belongs to the category of extremely complex technical and organizational systems, the management of which is currently practically impossible within the framework of previously established traditional approaches. The complexity of the transport infrastructure and its facilities (railway junctions, stations, transport corridors, etc.) fundamentally excludes the possibility of working in a fully automatic mode. In other words, it is impossible to effectively manage such a system only with the involvement of classical methods for solving complex mathematical modeling problems, search and development of new approaches are required. At the same time, great hopes are placed on intelligent systems that, along with accurate mathematical models, use data and knowledge accumulated in the course of their activities. The work of such systems can, and sometimes should, be based on the formalized experience of highly qualified specialists. Proceeding from this, JSC «Russian Railways» now needs to develop the fundamental foundations for the creation of intelligent railway systems using complex interdisciplinary approaches that can find practical application in a short time.

      Special attention should be paid to the fact that railway transport management systems, as well as complex systems in general, are characterized by fundamental inaccuracy and uncertainty in both data and management decisions. This makes it possible to attribute such systems from a mathematical point of view to the class of incorrect tasks and makes it possible to evaluate the quality of technical and managerial decisions in a different way. In this case, the promptness of the decisions taken plays a greater role than their optimality, understood in a strict mathematical sense. This quality is an important property of intelligent systems [14,15,16].

      In recent decades, there has been an active development and research of formal methods of working with uncertain data. Until recently, probability theory was the main instrument for accounting for uncertainty. However, the axiomatic limitations associated with it do not allow us to adequately apply probabilistic approaches to solving many important problems in which uncertainty has a different nature or properties. For example, the uncertainty of the events under consideration does not always have a frequency character, objective difficulties often arise with the formalization of a specific probability space, in many cases assumptions about the additive nature of a probability measure are difficult to explain, and sometimes simply unacceptable. For these reasons, at present, along with probability theory with its developed mathematical apparatus, new theoretical approaches to the description of uncertainty and incompleteness of information are actively being investigated. Here, first of all, we should mention the Dempster – Shafer theories, possibilities, interval averages, monotone measures. These theories have less rigid axiomatics, which allows, along with the frequency interpretation of events, to describe events whose uncertainty may be subjective (for example, the probability is determined by a number reflecting the subjective degree of confidence in the event), or in which the number of observed realizations does not allow obtaining reliable conclusions in a statistical sense.

      An important area that can have real practical application in the railway industry when creating ITS is the development of expert systems, i.e. computer programs that can fully or partially replace a specialist expert in some, as a rule, rather narrow problem area. Expert systems began to be developed by artificial intelligence researchers in the 1970s, and already in the 1980s they found their commercial applications. Expert systems function mainly together with knowledge bases, which are a set of facts and rules of logical inference in the chosen subject area of activity. This allows, in general, to model the behavior of experienced specialists in a certain field of knowledge using logical inference and decision-making procedures.

      A person, unlike a computer, has fuzzy thinking, effectively operates with variables not only quantitative, but also qualitative. Therefore, expert systems that model the style of human reasoning are especially successfully used in solving complex problems associated with the use of hard-to-formalize knowledge. It is important to understand that the creation of a specific expert system is a long and expensive process that СКАЧАТЬ