## Methodology
### Methods  
The profile of the Slovak and Czech truck drivers is determined using two-step cluster analysis. In detail, this tool generates homogeneous groups with similar driving behavior. Both continuous and categorical data are analyzed using the two-step cluster approach. It is possible to analyze quantitative variables simultaneously with multiple scale units and nominal scales. It is assumed throughout the procedure that categorical variables have a multinomial distribution, continuous variables have a normal distribution and all variables are independent. Additionally, the automatic calculation of the ideal number of clusters is included in the added benefits. Larger samples with more than 500 respondents are used with this technique [34]. Cluster analysis is quite often used in scientific research [35; 36; 37]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Trucker Drivers Engaged in**} \\ \hline  & & & **Domestic** & **International** & **Domestic and** & \\  & **Country** & & **Road Freight** & **Road Freight** & **International** & **Road Freight** & **Total** \\  & & & **Transport** & **Transport** & **Transport** & **Transport** & **Transport** \\ \hline  & & & 20–30 years & 14 & 18 & 27 & 59 \\  & & 31–40 years & 27 & 30 & 35 & 92 \\ \cline{3-6} SK & Age & & 41–50 years & 18 & 29 & 29 & 76 \\  & & 51–60 years & 4 & 18 & 14 & 36 \\  & & 61–70 years & 3 & 6 & 3 & 12 \\ \hline  & & Total & & 66 & 101 & 108 & 275 \\ \hline  & & & 20–30 years & 4 & 5 & 7 & 16 \\  & & 31–40 years & 12 & 23 & 16 & 51 \\ CZ & Age & & 41–50 years & 18 & 31 & 22 & 71 \\  & & 51–60 years & 12 & 28 & 16 & 56 \\  & & 61–70 years & 0 & 1 & 2 & 3 \\ \hline  & & Total & & 46 & 88 & 63 & 197 \\ \hline  & & & 20–30 years & 18 & 23 & 34 & 75 \\  & & 31–40 years & 39 & 53 & 51 & 143 \\ Total & Age & 41–50 years & 36 & 60 & 51 & 147 \\  & & 51–60 years & 16 & 46 & 30 & 92 \\  & & 61–70 years & 3 & 7 & 5 & 15 \\ \hline  & & Total & & 112 & 189 & 171 & 472 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The initial sample. The procedure includes several parameters, such as the distance measure, optimal cluster number and cluster quality [38]. \[\mathrm{d}_{\mathrm{(i)(j)}}=\xi_{\mathrm{i}}+\xi_{\mathrm{j}}-\xi_{\mathrm{(i, j)}} \tag{1}\]  
\[\xi_{\mathrm{s}}=-\mathrm{N}_{\mathrm{s}}\Bigg{(}\left(\sum_{\mathrm{k=1}}^{ \mathrm{K^{A}}}\frac{1}{2}\mathrm{log}\Big{(}\sigma_{\mathrm{k}}^{2}+\sigma_{ \mathrm{sk}}^{2}\Big{)}\right)+\left(\sum_{\mathrm{k=1}}^{\mathrm{R^{B}}} \hat{\mathrm{E}}_{\mathrm{sk}}\right)\Bigg{)} \tag{2}\]  
\[\hat{\mathrm{E}}_{\mathrm{sk}}\ =-\sum_{\mathrm{l=1}}^{\mathrm{L_{k}}}\left(\frac{ \mathrm{N}_{\mathrm{skl}}}{\mathrm{N}_{\mathrm{s}}}\times\mathrm{log}\Big{(} \frac{\mathrm{N}_{\mathrm{skl}}}{\mathrm{N}_{\mathrm{s}}}\Big{)}\right)\sum_{ \mathrm{l=1}}^{\mathrm{L_{k}}}\left(\frac{\mathrm{N}_{\mathrm{skl}}}{\mathrm{N }_{\mathrm{s}}}\times\mathrm{log}\Big{(}\frac{\mathrm{N}_{\mathrm{skl}}}{ \mathrm{N}_{\mathrm{s}}}\Big{)}\right) \tag{3}\]  
where  
\(\mathrm{K^{A}}\)--total number of continuous variables;  
\(\mathrm{K^{B}}\)--total number of categorical variables;  
\(\mathrm{L_{k}}\)--number of categories for the k-th categorical variable;  
\(\mathrm{N_{s}}\)--total number of data records in cluster s;  
\(\mathrm{N_{skl}}\)--number of records in cluster s whose categorical variable k takes l category;  
\(\sigma_{\mathrm{k}}^{2}\)--estimated variance of the continuous variable k;  
\(\sigma_{\mathrm{sk}}^{2}\)--estimated variance of the continuous variable k in cluster j;  
\(\mathrm{d_{\mathrm{(i)(j)}}}\)--distance between i and j;  
\(\mathrm{(i;j)}\)--index representing cluster formed by combining clusters i and j [39]. To determine market segmentation, product bundling, formal classification and medical diagnosis, two-step cluster analysis is frequently employed [40]. The ideal number of clusters. The cluster with the lowest BIC score is considered ideal.