# Risk Assessment for the Development of Emergency Scenarios for Tram Driver Training
## 3 Methodology
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Symbol** & **Description** & **Fuzzy Value** \\ \hline Low & R1 & \begin{tabular}{c} The occurrence of the event is almost impossible. The \\ consequences are insignificant. No impact on traffic flow. \\ \end{tabular} & \((0,\ 0,\ 1.5,\ 2.5)\) \\ \hline Minor & R2 & \begin{tabular}{c} The occurrence of an event is possible with limited/not \\ significant consequences—little impact on traffic flow. \\ \end{tabular} & \((1.5,\ 2.5,\ 3.5,\ 4.5)\) \\ \hline Medium & R3 & \begin{tabular}{c} The probability of an event occurring is almost inevitable. The \\ consequences are minor or negligible, or the consequences are \\ high. However, the event is unlikely to occur. \\ \end{tabular} & \((3.5,\ 4.5,\ 5.5,\ 6.5)\) \\ \hline Intermediate & R4 & \
\begin{tabular}{c} An almost certain or certain event that occurs with minor \\ consequences or the unlikely event that may have \\ critical consequences. \\ \end{tabular} & \((5.5,\ 6.5,\ 7.5,\ 8.5)\) \\ \hline High & R5 &
\begin{tabular}{c} There is almost certain to be an event with the most severe \\ consequences, which in the real world prevents the driver \\ from temporarily carrying out his work. \\ \end{tabular} & \((7.5,\ 8.5,\ 10,\ 10)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Description of the linguistic variables—risk level. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No. ** & **PSO** & **CSO** & **RL** \\ \hline
11 & P1 & C3 & Minor \\
12 & P2 & C3 & Minor \\
13 & P3 & C3 & Medium \\
14 & P4 & C3 & Intermediate \\
15 & P5 & C3 & Intermediate \\
16 & P1 & C4  
A schematic of the risk assessment model of training scenarios for tram drivers is presented in Figure 9.  
The estimated risk indicators were adopted for evaluation to determine undesirable events that should be considered when preparing emergency scenarios. The basis of the evaluation process is determining the risk acceptance level. Based on brainstorming among experts, it was assumed that the risk acceptance level should be at most four. This means that for all adverse events with a risk index higher than four, emergency scenarios should be developed as part of the driver training program. The use of fuzzy logic, compared to other risk assessment methods, has several advantages, especially in the case of the analysis of uncertain and subjective data, such as those that formed the basis of the risk assessment in the presented study. Traditional risk analysis models based on classical probabilistic methods require precise and unambiguous input data, often unavailable or difficult to measure in actual conditions. Meanwhile, in applications such as assessing training scenarios for tram drivers, the data may be characterized by a high level of uncertainty or subjectivity, resulting from the need to rely on expert assessments or historical events, which may not be fully representative of all possible scenarios. Fuzzy logic allows for introducing vague variables into the risk assessment process, which increases the flexibility and adaptability of the model concerning  
Figure 8: Input variable—risk level. Figure 9: Diagram of the risk assessment model of training scenarios for tram drivers. the actual context of risky situations [39; 40]. This allows for a more precise representation of the real conditions in which tram drivers may find themselves while performing their duties, which promotes a better representation of dynamic and often ambiguous situations. In the case of the conducted research, fuzzy logic enabled multi-level expert analysis of the probability of occurrence and potential consequences of adverse events, a key element of risk assessment in VR simulation-based training. Fuzzy logic also improves the accuracy and usability of risk assessment results by creating more scalable and realistic training scenarios.