Intelligent transport systems development

The national transport policy of many developed countries is currently based on the development and promotion of intelligent transport systems (ITS). They are considered as an effective means of solving urgent problems of the transport industry, such as an unacceptable level of human losses as a result of transport accidents, delays in the turnover of passengers and cargo, insufficiently high productivity of the transport system, 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 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 system’s 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. Let’s consider this provision in relation to regional (road) control centers – RTCC.

In each RTCC, a hierarchical dispatching structure solves tasks of three main types:

1) ensuring loading in accordance with the daily and current loading plans;

2) ensuring the passage of trains (including those performing local work) in accordance with the traffic schedule, the formation plan and the plan for the transfer of wagons along internal and external joints with unconditional compliance with traffic safety;

3) performing various kinds of special transportation and tasks.

There are obvious direct relationships in the work of various dispatchers when implementing these types of tasks. Close relationships also occur when solving tasks of various types, so delays in the passage of trains (task type 2) may entail non-fulfillment of tasks for tasks of types 1 and 3. Untimely completion of a special task (task type 3, for example, the promotion of a train with oversized cargo) may cause disruption of the transfer of trains and wagons at the joints (task type 3) and loading plan (task type 1), etc.

Therefore, synchronous integrated intellectualization of the AWSs of the entire control unit of the RTCC is advisable.

The main provisions are defined, the implementation of which is a necessary condition for the intellectualization of management processes in regional dispatch centers. These include:

? the use of principles for the development of automated process control systems (TP ACS);

? ensuring efficiency in solving various types of tasks and resolving emerging conflict situations;

taking into account market conditions in the work of control centers;

? saving all kinds of resources.

When building management processes, it is necessary that the developed algorithms for solving specific tasks (for RTCC dispatchers these are operational tasks) make it possible to obtain rational, and if possible, optimal solutions. For this condition, it is necessary to have a sufficient amount of information about the processes, take into account the influence of various factors, including disturbing influences, as well as constantly monitor the situation on the basis of special feedback subsystems.

It is these requirements that are taken into account when building TP ACS as closed control systems with feedback.

Each dispatcher constantly accumulates experience, which is used when making decisions. Therefore, when developing intelligent systems, it is important to use the principle of their self-learning.

At the present stage of development of intelligent RTCC systems, the control solutions developed should be used in the «adviser» mode. With the accumulation of experience in the operation of such systems, the refinement of the complex of factors and algorithms taken into account, the transition to the automatic mode of their operation will be carried out.

The dispatcher’s work proceeds in the constant adoption of operational decisions. The degree of efficiency depends on the needs and capabilities of forecasting specific situations.

The need for an operational forecast can extend over a very long period. Let’s imagine the situation in a RTCC, the scope of which includes a large seaport, and the cargo comes from loading stations located at distances of several thousand kilometers. Linking the approach of wagons with the approach of ships, especially taking into account weather conditions, requires a forecast of the operational situation for 10—15 days ahead.

A multi-day forecast is also required to solve the problem of organizing the turnover of locomotives and locomotive crews. At the same time, a forecast for 20—30 minutes may be sufficient for the train dispatcher to solve a specific conflict situation of train traffic on the section.

Therefore, for each task performed in the RTCC, the developer of an intelligent management system determines the required forecast period and the real possibilities of obtaining it based on relevant information, including those available in existing databases (APOMS-2, etc.).

In the classical formulation, the well-known problem of the distribution of empty wagons is considered as a transport problem of linear or dynamic programming with cost optimization at a minimum of wagon-kilometers. If the «just in time» condition is met, it is necessary to take into account the additional condition of dynamics in terms of the time of receipt and the time of «consumption» (feeding for loading) of empty wagons. Developers of intelligent systems should take this into account.

In market conditions, guaranteed delivery of goods is one of the main tasks of JSC «Russian Railways». This is the most important indicator of the quality of the company’s products, its competitiveness.

It is required to develop an intelligent system with the objective function of minimizing fines paid by JSC «Russian Railways» due to late delivery of goods. The methodology for solving this problem within the framework of the RTCC should be based on the ranking of wagons arriving at the railway (in the region) with varying degrees of delay in relation to the delivery dates, determining regulatory measures to accelerate the promotion of such wagons, taking into account the degree of their delay, developing proactive measures for wagons with possible violations of the delivery time of goods.

Some of the stated provisions have already found practical implementation.