Information, information systems, and technologies encompass a very broad field. Today, information is classified alongside material, energy, and financial resources as one of the fundamental factors determining progress—not only technological but also advancements in other areas of human activity [63,64]. Information flows within systems create essential connections and couplings between elements and entire systems in complex technological structures [10,25,33,63]. Without a certain level of information, it is impossible to create or manage processes in technical works and human society [16].
This dissertation focuses primarily on the traffic management system of the Prague metro and the informational support for enhancing safety through the use of information technologies, i.e., increasing information performance. This ensures accurate and timely decision-making based on the degree of knowledge, which is particularly crucial under abnormal and critical conditions. By doing so, the information performance enhances the safety of the system. The following paragraphs describe the use of information systems, the theory of information generation, the parameters of the applied technologies, the degree of information, information performance, and the security of information systems.
A more detailed description of the issues related to information systems and technologies in the field of technical works safety management can be found in [10,16,65]
2.4.1 The Use of Information Systems in Railways
Information technologies and systems serve as tools for management or, in the case of automated operations, independently manage both qualitative and safety parameters. Information systems and technologies are an integral part of railway systems.
Table 3 presents examples of the use of information systems in various areas of railway transportation, including metro operations, which are the focus of this dissertation, according to [65].
Table 3 Examples of the use of information systems in railways [65]
Area of Application
Used for
Management and Planning
Evaluation of operational data, creation of timetables, staff duty rosters, decision-making, economic, and accounting activities, communication with emergency services and police
Traffic Management
Central monitoring and control, dispatcher activities, station and track technologies, data collection and processing along train routes, communication between stationary and onboard systems, signaling equipment
Train Operation
Train control, onboard computers, data transmission between onboard devices, monitoring and controlling train systems (doors, air conditioning, train intercom, power systems), interface between technician and train driver
Passengers:
Information boards, passenger ticketing systems, onboard entertainment systems, Wi-Fi, navigation systems – directional signs, systems for the disabled
Table 3 Examples of the Use of Information Systems in Railways [65]
2.4.2 The Process of Information Generation
The generation of information is conditioned by observing certain properties of the object under observation or shared properties of a group of objects. Every information system monitors the properties of entities using a specific language, which serves to create information about the observed object [64,65]. Depending on how the information obtained is interpreted, different types of information systems are distinguished [64,65]:
syntactic information systems, which create a set of informational representations of the state variables of the observed object,
process information systems, which represent a set of processes
Action information systems, through feedback, influence the original observed object [64], [65]. For the purposes of this dissertation and within the scope of railway traffic management, action process information systems are primarily applied.
The process of information generation, the creation of an information system, the process of creating a new object or modifying an object, consists originally of the following subprocesses, or a set of links and their relations [64], as described in Table 4.
Table 4 The Process of Information Generation and Information Technology [65]
Subprocess of information generation / set of objects
Affected abstract nodes
Used information technologies
Process inputs
Process outputs
1
Object identification
Object, observer
Physical receptors (sensors, detectors)
Observed state (physical) variables of the object
Signals
2
Observation
Observer, language (syntax)
Sampling, quantification, encoding/decoding
Signals
Data
3
Communication between source and recipient of the message
Language (observer or data collection system), message recipient
Telecommunication, transmission, and communication systems
Data
Data
4
Interpretation set, generation of information
Language (observer or data collection system, or recipient), information set (line 6)
Ontology, language
Data
Information
5
Relationships of functions and structural arrangement of the object, verification of integrity
Information set (line 6), object
Action element of the system, action information system
Object, information
Correctness of information, change of object
6
Set of information in the set of information systems
Information systems
Information systems
Information
Information
7
Interpretation process
Information set (line 6), new object
Signaling and information representation technologies, artificial intelligence
Information
Image of the object, new object
The process of generating an informational image can also be expressed using Frege’s functional concept of the generation of an informational image [64], which consists of the following sets:
Oi – set of state variables on the object,
Pi – set of states (observers),
Φi – set of syntactic strings (data flow),
Ii – sets of informational images of state variables,
and their mutual relationships described in the work [64], which determine the quality of the process of generating the informational image:
OP – identification,
PO – invasiveness (risk of disruption of the integrity of state variables on the object),
PΦ – projection in the set of symbols and syntactic strings,
ΦP – correction and identification of indeterminacy,
ΦI – interpretation, generation of information,
IΦ – reflection of language constructs,
IO – relations of functional and structural arrangement,
OI – verification of integrity.
From Table 4 and according to the source [64], the following definitions can be derived:
data – uninterpreted data about the state of the object,
information – interpreted data, information, signals leading to a change in the arrangement in real-world systems or consciousness.
2.4.3 Qualitative Properties of Information Systems and Technologies
The qualitative properties of information systems and technologies can be influenced by appropriately setting their parameters, such as the amount of information determined by the number of possible messages limited by the number of characters, and the parameters of the transfer matrix defined by formula (11) below, which determines the system’s interpretative and filtering ability, communicative capacity, and information throughput [65].
In practice, it is essential to evaluate the “size of information” [65]. The measure of information according to [64] is most commonly characterized by Hartley’s measure of information for a binary symbol system (i.e., for most current cyber-physical systems). It is expressed by the relation:
(8)
where N represents the number of possible messages (data):
(9)
where S is the number of characters in the alphabet A (A1,A2,…AS), and n is the number of elements in the set of characters.
Process information systems are characterized according to [64] by graphs assigned to relations:
(10)
where Ii=1,2…n represents the informational image of state variables, the set of states P, and syntactic strings (information flows) ϕ over time t.
The given assignment enables the structural interpretation of complex information systems, evaluation of feedback, and the quality of information transfer and processing in individual information systems [64], and its information segment is based on matrix representation [65]:
(11)
where Ti is the transfer matrix of the i-th information segment (i.e., the segment of information performance).
In a real system, the relationship (10) implies that information or a set of information Ii is connected to the set of system states and information flows over time. The information segment in relationship (11) can be assigned, for example, to a data collection system, where I1 represents input (initial) information, ϕ1 the input information flow, and on the other side of the system I2 as the input information and the transferred information flow ϕ2[65]. Parameters t can be obtained both quantitatively and qualitatively [63,65], and in the given example according to source [64], they represent:
ta – interpretative ability (for ta < 1, the system has very low knowledge and interpretative ability, for ta=1, it has the ability to interpret the properties of an object in the information system, and for ta > 1, it represents an expert system with the ability to represent its information about an object based on acquired data),
tb – filtering ability (in the case of tb < 1, the system interprets less information on its output than it receives on its input information flow, and for tb > 1, the opposite is true),
tc – communicative ability (the ability to provide output information flow based on input information),
td – information system throughput (the ability to convert input information flow to output information flow; in the case of redundancy, td is much greater than 1).
2.4.4 Information Performance and Its Relationship to Safety
To ensure the safety of a system of systems, it is crucial to create such couplings between systems that provide a high level of connection quality, i.e., qualitative system parameters [65], which according to [25,63], include:
safety at the system-of-systems level,
integrity of measures,
system-of-systems reliability,
quality of active and passive measures enhancing the safety of individual systems,
availability of a specific system or device whenever needed,
continuity of the measure application process,
accuracy in measure execution.
These system parameters are directly dependent on the effectiveness of information systems that ensure the required accuracy and timeliness of information, and in the case of action information systems, also the speed of correct decision-making. The effectiveness level of an information system is expressed by the quantity of information performance [63-65]:
(12)
where Pi(t) is the instantaneous information performance [63-65]. Information performance P represents the content of the transmitted decoded message I in the information flow Φ. Information performance also equals the extent of uncertainty reduction E in knowledge (i.e., the physical quantity of work) per unit of time t[64]:
(13)
For complex systems, due to the heterogeneity of information types (quantity, content, accuracy, validity), it is difficult to clearly quantify information performance. Therefore, it is essential to continuously improve information performance to ensure the highest quality level of control system functionality [63-65]. Several methods are used in practice to enhance information performance and minimize resources needed for system development, including [63-65]:
COBIT for auditing information systems from the perspective of executive management [66],
ITIL for managing information systems and services,
refactoring, i.e., changes to the software system that improve its internal structure and resource utilization without affecting its external functional behavior [64].
To ensure the safety of control systems, it is therefore important to operate information systems that provide the fastest and most accurate decisions, which are closely linked to information performance [64].
The probability of choosing the correct alternative solution from a set of solutions and the probability of correct decision-making in control system operation, i.e., PCD (Probability of Proper Decision), is determined by a function dependent on the level of knowledge of the function “k” and the information flow over time “Φ ( t )” [63-65]:
CD = F [ Φ ( t ), k ].
(14)
Thus, control systems relying on a higher level of knowledge can make faster correct decisions with less load, i.e., they decide faster [65].
Properly selected parameters of information technologies and systems ensure their information performance, i.e., the quality of information that enables effective system responses to potential undesirable conditions. This improves railway safety not only under normal conditions but also under abnormal and especially critical conditions [63,65].
2.4.5 Quality of the Transmission System
Given the nature of the task addressed in this work, specifically a distributed transportation system with geographically distant system nodes, it is appropriate to present the relationships describing the quality of information transfer within the transmission environment (Process 3 according to Table 4). Let us consider a feedback control system represented in Figure 8, in accordance with [10,67,68].
**Figure 8. Connections of the control system in a cybernetic system according to [68].**
The quality of the functionality of the mentioned cybernetic system is therefore influenced by two main factors [10]:
the correct behavior of the system and the control center,
the correct data transmission between the nodes of the cybernetic system.
For an exact mathematical description of both factors, i.e. the individual communication channel and the entire cybernetic system, we will use the model of the Gaussian transmission channel [69] and Bayes’ theory (conditional probability theory) [10,23].
Figure 9. Gaussian transmission channel [69].
For a memoryless communication channel (regardless of the output at a time smaller than point t) according to [10], the following applies:
(15)
For a communication channel with memory (e.g., a channel with feedback), with data until time M, according to [10], the following applies:
(16)
For the cybernetic system described in Figure 8 with parameters
the following relationships were referenced in [10]:
system:
(17)
control center:
(18)
communication environment A:
(19)
communication environment B:
(20)
2.4.6 Cyber-Physical Systems Security
Parallel to the increase in information performance, in practice, and especially in the case of critical infrastructure, the demands on security and protection also increase. The security of cyber-physical systems lies in securing the system’s information so that the controlled system can perform its functions safely, i.e., that its malfunctions do not endanger itself or its surroundings. The process of ensuring information security (i.e., its protection) involves protecting critical assets of the cyber (information) system so that the required level of availability, integrity, and confidentiality (known in English as CIA – Confidentiality, Integrity, Availability) is ensured for important information [52,65]. Unlike the system approach described in the previous chapter, from the perspective of information technology, ensuring CIA according to [52] means ensuring:
Confidentiality – information is not available or revealed to unauthorized individuals, entities, or processes,
Integrity – information is complete and accurate,
Availability – accessibility and usability of information always upon request by an authorized entity.
Availability and integrity can be expressed, for example, using probabilistic parameters in relationships (15) to (20) presented above.
Confidentiality includes additional attributes and methods for ensuring it. Often, these methods are in contrast to ensuring availability and integrity because ensuring confidentiality increases, for example, the time requirements for encoding and decoding, transmission, authentication, and similar processes [52,65]. In the case of process information systems in transportation that operate in real-time, compared to other information systems, the primary requirements are for availability and integrity, whereas confidentiality is not as high a priority [52,65].
To ensure the quality, information performance, and security of cyber systems, approaches from process and project management such as TQM [8] are applied, from which the methodologies and international and European standards for management systems [10,63,65] are derived. The purpose of these management systems is to find economically efficient processes that ensure a certain level of quality and security for cyber-physical systems, particularly in the phases of their design, analysis, development, operation, and recovery and disposal.
Each system operates correctly only under certain conditions, i.e., surrounding conditions. Therefore, cyber-physical systems must have certain limits and conditions established that condition their qualitative parameters (i.e., safety, reliability, availability, integrity, continuity, and accuracy) [22,41].
The design of the process model for securing cyber-physical systems is presented, for example, in sources [10,63,65,70,71].
