## 3 Materials and Methods
Accordingly, the system's digital twin makes it possible to obtain the reference parameters of its status during the life cycle of the system at a given point in time; these parameters can then be compared with the current actual parameters. Deviations exceeding the permissible value can be interpreted as failures. Accordingly, the digital twin itself is described by the following formula [48]:  
\[\text{M}_{\text{DT}}=\text{(PE, VM, Ss, DD, CN)} \tag{4}\]  
where PE are physical entities; VM are virtual models; Ss are services enabling management; DD are data resulting from digitalizing the interaction of physical entities; CN are the connections between physical entities. A significant challenge in implementing such an architecture is to provide semantic links between data from different sources obtained through digitalization and the business activities of a company that are subject to various regulations [49]. In the road transportation industry, compliance with the requirements of these regulations is a vital necessity associated with minimizing various types of risks. In engineering, an ontological approach is used to analyze technical failures, where ontology is applied to establish semantic relationships between different types of failures, their causes, or combinations of causes and the consequences of failures [50; 51]. For example, ontology data are used for the diagnostics of various equipment, including on-board electronics and mechanical systems. To analyze transportation process reliability, Ref. [52] proposed a classification of possible failure locations at different stages. Refs. [53; 54] provided a classification of failures by their causal factors, as well as their features and manifestations. Notably, the share of failures related to human factors in some way or another reached over 50%. Developing this classification and subsequent analysis of transportation risks and transportation process reliability management, it is proposed to classify events and situations related to the manifestation of failures by their sources: material resources (vehicles, cargo); humans (the driver and dispatchers); external environment (natural and climatic conditions); and infrastructure (road conditions). Based on this classification, an ontology is created to describe different factors and their impact on the reliability and variability of the transportation process, as well as different situations and responses to them (Figure 3). The natural and climatic conditions of vehicle operation are characterized by the ambient air temperature, atmospheric pressure, wind speed, amount of precipitation, duration of the winter period, and some other factors affecting the reliability of transportation primarily in the case of long-distance trips through remote locations that are far away from populated areas. For example, at low temperatures, fuel consumption increases significantly, which in turn can lead to increased cargo transportation time due to additional refueling during the trip. On longer routes, typical for such Russian territories as Siberia, the north Urals, and the Kola Peninsula, where the temperatures are extremely low, high fuel consumption might lead to critical consequences as the vehicle might halt on the road sections remote from gas stations. For example, at \(-25\)\({}^{\circ}\)C, fuel consumption may exceed the baseline rates by 11%. In addition, weather and climatic conditions (snow, rain, wind, low temperatures) also affect vehicle speeds, which is also observed in several other countries with harsh climates (Canada, Finland). Human errors, tardiness, or misunderstanding leading to the distortion of information in the documents accompanying the transportation process; inefficient or untimely actions; as well as the inaction of dispatchers are important factors affecting the reliability of transportation. Figure 3: Fragment of the transportation reliability ontology.