## 5 Results and Discussion  
The considerations described in Chapter 4 were adapted in the modelling environment. To demonstrate the applicability of the elaborated concept, we present numerical examples using the network representation introduced in Figure 1.  
Regarding the applied square lattice, we considered the side of the elementary regions to be 5 m long (based on the dimensions of an average passenger car). We considered the size of the trucks to be twice the size of the passenger car. Accordingly, the length of the cells in the alternative representations was twice the length of the cells used in the original model. The value of the offset was the size of a region (5 m). The total number of locations of the original representation (\(o_{O}\)) is 16, while the number of all locations of the alternative mappings (\(o_{A}\)) is 32. The initial data are presented in Table 1. In addition, the value of the speed limit was chosen to be 15 m/s, the limit of both the acceleration and deceleration was set to 10 m/s2. The calculations were performed applying three different traffic demand structures. In the case of the first demand structure (\(DS1\)), we optimized the routes of 10 passenger vehicles and 2 trucks, with randomly selected origin and destination locations within the network. Subsequently, in the second and third demand structures, the proportion of trucks within the total traffic volume was gradually increased (in \(DS2\), 4 out of the 12 examined vehicles were trucks, while in \(DS3\), this number increased to 6). Table 1 provides an overview of the vehicles' origins and intended destinations. The starting and destination locations of the trucks were defined on the alternative representations (the corresponding regions of the original representation are also indicated in brackets in these cases). The optimization processes were performed using the MATLAB R2023a software, the number of the modelled time moments (\(t\)) was set to 10 s in all three cases. Based on the results of the optimization, the modelled junction was emptied during the considered timeframe for all demand structures. The resulting routes per vehicle were summarized in the columns of Tables 2-4. In each of the three defined traffic demand structures, we optimized the routes of 12 vehicles. In the first case, we modeled 10 passenger cars with varying origins and destinations, along with 2 trucks departing from the same location. In the subsequent scenarios, the proportion of trucks was increased, with these vehicles always starting from the same location. \begin{table}
\begin{tabular}{l c c c c c c} \hline  & \(DS1\) & & \(DS2\) & & \(DS3\) \\ \hline \(m_{p}\) & 10 & & 8 & & 6 \\ \(m_{h}\) & 2 & & 4 & & 6 \\ \hline  & Origin & Destin. & Origin & Destin. & Origin & Destin. \\ \hline \(k=1\) & 1 & 13 & 1 & 13 & 1 & 13 \\ \(k=2\) & 1 & 15 & 2 & 13 & 2 & 14 \\ \(k=3\) & 2 & 13 & 2 & 14 & 2 & 15 \\ \(k=4\) & 2 & 14 & 2 & 15 & 3 & 15 \\ \(k=5\) & 2 & 15 & 3 & 14 & 3 & 16 \\ \(k=6\) & 3 & 14 & 3 & 15 & 4 & 16 \\ \(k=7\) & 3 & 15 & 3 & 16 & 17 (1) & 33 (13) \\ \(k=8\) & 3 & 16 & 4 & 16 & 17 (1) & 34 (14) \\ \(k=9\) & 4 & 14 & 17 (1) & 33 (13) & 17 (1) & 33 (13) \\ \(k=10\) & 4 & 16 & 17 (1) & 34 (14) & 17 (1) & 34 (14) \\ \(k=11\) & 17 (1) & 33 (13) & 17 (1) & 33 (13) & 17 (1) & 33 (13) \\ \(k=12\) & 17 (1) & 34 (14) & 17 (1) & 34 (14) & 17 (1) & 34 (14) \\ \hline \end{tabular}
\end{table} 
Table 1: Initial constant data.