## 2 State of the Art
Studies [22; 23; 24; 25] focus on state monitoring to improve reliability, traffic security and RTC system availability. Neural networks were used to detect and diagnose failures in a typical track circuit, as described in [25]. Refs. [22; 23] discuss the diagnostics of failures in rail signalling systems with a constant block section. Petri nets were employed in this case. The source literature contains publications on onboard train steering and control systems (which have been used within the railway industry for some time now). The authors of [24] proposed a method for diagnosing failures of onboard devices using the rough set theory, and [26] is based on an example of the European Train Control System (ETCS) for Better Energy Efficiency Using a Timed and Asynchronous Model. The authors of [27] describe principles of conducting functional field and laboratory tests for railway traffic control devices. In their study, they focused on tests implemented under normal (fault-free) conditions and with failures of the signalling device setting circuit, such as gaps and short circuits between signal chamber feeder wires. In the course of their work, they presented the impact of cable network parameters depending on weather conditions on the measurements of the voltage and current drawn by signalling bulbs. Pursuant to [1; 5; 8], this type of test is also solely required within the certification process as a study of system responses to failures and to prove system security (safety testing). Rail traffic control devices are responsible for safe train traffic throughout a railway network. They protect against collisions and dangerous situations by setting routes and monitoring travel. In turn, traffic control devices are responsible for monitoring and controlling the traffic situation and timetable implementation. Control or monitoring device unavailability significantly impacts the implementation of the traffic process. Unexpectedevents require a change in the timetable as they arise, as demonstrated in [28; 29; 30; 31], solving traffic disruption issues on a micro- and macro-scopic scale through the application of mesoscopic mixed integer linear programming (MILP) [28]. A similar approach was presented in [29], the authors of which propose improvements to basic functionalities of automatic train control (ATC) systems, in particular, the automatic train supervision (ATS) module, which is integrated in ATC systems. Real-time control is based on the model predictive control (MPC) pattern, which solves the schedule change problem through mixed integer linear programming using macro- and meso-scopic models. The article [30], accordingly, demonstrates a decision support system (DSS) for real-time management of railway traffic networks. Here, the approach of mathematical programming to reschedule a traffic timetable in the event of unexpected traffic disruptions was applied by simulating traffic behaviour through a mathematical programming model based on railway line topology and constraints, as well as real-time changes of the timetable while simultaneously detecting and solving conflicts. The authors of [31] presented a self-learning decision-making procedure aimed at changing train timetables in real time in the event of disruptions. A modified timetable is obtained by solving a mixed integer linear programming problem and finding an optimal compromise between two objectives, namely, train delay minimization and timetable reliability maximization. The aforementioned papers focus on generating new train timetables resulting from various undefined disruptions. Such an approach is equally important from the perspective of timetable punctuality and implementation, which is associated with restoring nominal rail traffic timetables. However, these actions focus on mitigating the effects, which result from device unavailability. In contrast, the method we present minimizes the cause behind disturbance when executing functional tests covering rail traffic control devices, which directly impacts the reduction of traffic perturbations.