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 requires the involvement of specialists in various fields programmers, knowledge engineers, experts in the field of application under consideration. One of the main problems in this case is the formation of knowledge, which is transmitted during numerous interviews of a knowledge engineer and an expert in the subject area. The stage of knowledge acquisition is one of the main bottlenecks in the technology of creating expert systems due to the low rate of filling the systems knowledge base. It should be added to this that there are subject areas for which it is often difficult to find an experienced expert person, and sometimes there simply does not exist one. In addition, it has long been noticed that not all experts are ready and able to share their knowledge [2,8.10].
An important quality of technical systems that allows them to be classified as intelligent is the presence of such properties as:
learnability the ability to generate new knowledge and data (models, decision rules) based on inductive inference mechanisms, generalization of statistical data, etc.;
classification ability the ability of the system to independently differentiate control objects, environmental influences, control signals, automatically structure data;
adaptation the ability of the system to adapt to the changing conditions of the operating environment, correctly take into account the non-stationarity of control data, etc
One of the promising approaches to the creation of intelligent systems may be to attract the ideas of situational management as a system wide approach based on formal methods of theoretical artificial intelligence logical-linguistic models, models of learning technical systems in the construction of management procedures for current situations, deductive systems for building multistep solutions, etc. In this important area of research, as well as in the development of general methodology, theoretical foundations and specific applications, priority undoubtedly belongs to Russian scientists.
The problem of industrial implementation of intelligent information systems capable of processing data with their inherent a priori uncertainty in railway transport is becoming more and more urgent. In many cases, the data is not only inaccurate and uncertain, but also incomplete, and sometimes unreliable. The development of methods that allow obtaining reliable conclusions based on such data is another direction for fundamental research.
4 AUTOMATED DISPATCH CENTERS AS INTEGRATED INTELLIGENT TRANSPORTATION PROCESS MANAGEMENT SYSTEMS
Currently, the development of various automated control systems in railway transport is increasingly taking place in the direction of their intellectualization. As a rule, intelligent railway systems are created to control individual processes.
World experience shows that the greatest effect is achieved when developing and implementing an integrated interconnected complex of intelligent systems. In this case, a unified information support is created, the mutual influence of managed processes is taken into account.
General integration principles
An illustrative example of the need to create an integrated complex of intelligent systems is the existing network (TCC) and regional (RTCC) automated dispatch control centers. There are dozens of automated workstations (AWSs) in various areas of organization of the transportation process, maintenance and repair of infrastructure and rolling stock devices, as well as security. Each AWS as a human-machine system performs a specific target function. However, a full-fledged interconnection of these functions can be carried out only with the integrated construction of a complex of intelligent dispatch systems. In principle, we can talk about a unified intelligent system in automated dispatch control centers. Lets consider this provision in relation to regional (road) control centers RTCC.