# Analysing Urban Traffic Patterns with Neural Networks and COVID-19 Response Data
## 4 Results
### Model Evaluation  
As mentioned in the study's methodological part, the data were randomly divided into training and testing sets to avoid overestimation. The above-described models were created on the training data, while the resting testing data were used for model evaluation. In Table 7, we present the evaluation characteristics of the created models on the testing part of the data. We also supplemented the table with statistical characteristics of the actual number of vehicles in the testing part of the data and the corresponding predicted number. By comparing the rows on the left side of the table, we can claim that the predictions, on average, correspond to the real numbers of vehicles. Figure 17 presents the graphical evaluation of the predictions from individual models for vehicle types. Each scatter-dot graph presents the dependence of predictions and the real values. The values close to the diagonal (yellow line) mean the model's high performance. Figure 17: Dependence of real and predicted number of vehicles. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Vehicle Type, Direction**} & \multicolumn{1}{c}{**Testing Part**} & \multicolumn{1}{c}{**Min**} & \multicolumn{1}{c}{**Max**} & \multicolumn{1}{c}{**Mean**} & \multicolumn{1}{c}{**Sid. Dev**} & \multicolumn{1}{c}{**Min Error**} & \multicolumn{1}{c}{**Max Error**} & \multicolumn{1}{c}{**Max Error**} & \multicolumn{1}{c}{**Mean Error**} & \multicolumn{1}{c}{**Mean Abs. Error**} & \multicolumn{1}{c}{**Sid. Dev of Error**} & \multicolumn{1}{c}{**Linear Correlation**} \\ \hline  & actual & 0 & 300 & 26.36 & 29.11 & \(-\)111 & 265 & \(-\)0.33 & 7.09 & 11.38 & 0.93 \\ \hline  & residual & 0 & 253 & 16.19 & 19.35 & \(-\)67 & 221 & \(-\)0.44 & 4.77 & 7.87 & 0.92 \\ \hline  & prediction & 0 & 89 & 18.03 & 18.22 & \(-\)67 & 221 & \(-\)0.44 & 4.77 & 7.87 & 0.92 \\ \hline  & actual & 0 & 26 & 1.73 & 2.68 & \(-\)11 & 235 & \(-\)0.07 & 0.99 & 1.00 & 0.73 \\ \hline  & prediction & 0 & 13 & 1.98 & 2.65 & & & & & & \\ \hline  & actual & 0 & 260 & 4.36 & 6.65 & \(-\)27 & 259 & \(-\)0.12 & 1.81 & 3.25 & 0.98 \\ \hline  & prediction & 0 & 31 & 4.09 & 6.06 & \(-\)188 & 240 & \(-\)0.31 & 8.68 & 14.19 & 0.93 \\ \hline  & actual & 0 & 300 & 12.63 & 16.35 & \(-\)64 & 297 & \(-\)0.23 & 4.22 & 6.92 & 0.91 \\ \hline  & prediction & 0 & 49 & 14.29 & 14.88 & \(-\)16 & 291 & \(-\)0.01 & 1.00 & 2.10 & 0.80 \\ \hline  & prediction & 0 & 17 & 1.92 & 2.80 & & & & & & \\ \hline  & actual & 0 & 300 & 3.86 & 4.57 & \(-\)13 & 230 & \(-\)0.05 & 1.83 & 2.88 & 0.80 \\ \hline  & prediction & 0 & 14 & 4.29 & 3.80 & & & & & \\ \hline  & actual & 0 & 300 & 26.36 & 29.11 & \(-\)99 & 252 & \(-\)0.35 & 8.09 & 12.83 & 0.90 \\ \hline  & actual & 0 & 253 & 16.19 & 19.35 & \(-\)60 & 217 & \(-\)0.40 & 5.47 & 8.79 & 0.90 \\ \hline  & prediction & 0 & 67 & 18 & 17.83 & \(-\)7 & 236 & \(-\)0.01 & 1 \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & **Vehicle Type. Direction** & **Testing Part** & **Min** & **Max** & **Mean** & **Sid. Dev** & **Min Error** & **Max Error** & **Mean Error** & **Mean Abs. Error** & **Sid. Dev of Error** & **Linear Correlation** \\ \hline  & actual & 0 & 200 & 4.36 & 6.63 & \(-\)24 & 200 & \(-\)0.14 & 2.06 & 3.55 & 0.86 \\ \cline{2-11}  & prediction & 0 & 27 & 5.00 & 5.80 & & & & & & \\ \cline{2-11}  & actual & 0 & 300 & 30.22 & 34.65 & \(-\)125 & 245 & \(-\)0.52 & 9.98 & 15.81 & 0.91 \\ \cline{2-11}  & prediction & 0 & 142 & 34.99 & 30.78 & \(-\)25 & 300 & \(-\)0.29 & 4.87 & 7.71 & 0.89 \\ \cline{2-11}  & actual & 0 & 300 & 12.63 & 16.35 & \(-\)25 & 300 & \(-\)0.29 & 4.87 & 7.71 & 0.89 \\ \hline  & prediction & 0 & 56 & 14.35 & 14.34 & & & & & & \\ \cline{2-11}  & actual & 0 & 201 & 1.70 & 3.31 & \(-\)13 & 291 & \(-\)0.03 & 1.07 & 2.21 & 0.79 \\ \cline{2-11}  & prediction & 0 & 15 & 2.00 & 2.67 & & & & & & \\ \hline  & actual & 0 & 300 & 3.86 & 4.70 & \(-\)13 & 200 & \(-\)0.01 & 2.04 & 3.15 & 0.75 \\ \cline{2-11}  & prediction & 0 & 14 & 4.00 & 3.65 & & & & & & \\ \hline \hline \end{tabular}
\end{table}