## 6 Conclusions  
Current directions of development of cellular networks, which cover areas of different topological characteristics with a signal, are oriented towards providing permanent Internet access for mobile users both in rural areas and along roads. The selected case study in this \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dependent Variable**} & \multirow{2}{*}{**Measurement/Group**} & \multicolumn{2}{c}{**Kolmogorov–Smirnov**} & \multicolumn{2}{c}{**Shapiro–Wilk**} \\  & & **Statistic** & **df** & **Sig.** & **Statistic** & **df** & **Sig.** \\ \hline \multirow{2}{*}{_Cell Downlink Average_} & 1. & 0.173 & 30 & 0.022 & 0.811 & 30 & 0.000 \\  & 2. & 0.161 & 30 & 0.046 & 0.924 & 30 & 0.035 \\  & 3. & 0.235 & 30 & 0.000 & 0.668 & 30 & 0.000 \\ \hline \multirow{2}{*}{_Average User Downlink_} & 1. & 0.129 & 30 & 0.200 & 0.944 & 30 & 0.117 \\  & 2. & 0.122 & 30 & 0.200 & 0.964 & 30 & 0.392 \\  & 3. & 0.226 & 30 & 0.000 & 0.816 & 30 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Results of the normality tests of the RE by repeated measurements. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Cell Downlink Average Throughput**} & \multicolumn{2}{c}{**Average User Downlink Throughput**} \\ \hline N & 30 & N & 30 \\ Chi-Square & 1.914 & Chi-Square & 1.270 \\ df & 2 & df & 2 \\ Asymp. Sig. & 0.384 & Asymp. Sig. & 0.530 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Results of Friedman test for two dependent variables. paper is the traffic segment M9], in RS, BiH with access roads with an explanation of the high importance of this road and its connection with the key telecommunications network, M:tel BL. The starting idea is related to the fact that the predictive modeling, as a procedure of using known data in creating models for predicting future results, is significantly improved with the adaptive modeling of the network performance. The specific example refers to the potential application of the connected vehicle concept, for interactive vehicle-to-vehicle communication and with road infrastructure (vehicle-to-infrastructure). Additionally, the developed model can be an important reference for designing a network of wireless sensors placed along motorways that can communicate within the LTE network technology, which are part of the CPS. This means that the presented results enable the mobile operator to better analyze network performance for the needs of future planning and design of traffic in next generation networks. The results of training and testing of 30 different variants of the MLP model, with different metaparameters showed that the final prediction model for the variable Cell Downlink Average Throughput has an average accuracy of 89.6%, while for the variable Average User Downlink Throughput the average accuracy is 88%. Additionally, observing the value of R2, it is concluded that in the case of the first dependent variable, the accuracy of the model is 1.4% higher than the accuracy of the model for the second dependent variable. The classical predictive models, such as the linear regression ones, are characterized by training speed and simplicity. The research in this paper confirms that adaptive models based on machine learning, including MLP, show their key advantages in better modeling the nonlinear functional dependence of output on input. It means dependent on independent variables, especially when their number is higher. When accuracy is taken into account, the superiority of the MLP model over the MLR model can be expressed quantitatively based on R2. The best MLP model for Cell Downlink Average Throughput prediction has 35.8% higher accuracy compared to the same MLR model. For the Average User Downlink Throughput variable, the advantage of the MLP model over the MLR is higher by 34.2%. A special advantage of the developed MLP model in the IBM SPSS technology is given by a large number of tabular and graphical results that significantly facilitate the understanding of the relationship between variables.