# Analysing Urban Traffic Patterns with Neural Networks and COVID-19 Response Data
## 5 Discussion  
As we mentioned in the Literature review section, neural networks are widely used machine learning techniques for modelling traffic and predicting the number of vehicles. In this study, we also created a functional model for the traffic in the city of Zilina in Slovakia, considering the situation during the COVID-19 pandemic. The impact of the pandemic on the number of vehicles was described by using the respective variables publicly available for each country. Therefore, the methodology described in this study applies to any other city worldwide. Thus, the study's contribution lies in the ease of updating the created neural network models using newly emerging data, thereby adapting mobility prediction to any changes in the situation after the pandemic ends or the circumstances in any other city. This opens up the possibility of further research, where the actual impact of the pandemic on mobility in the city can be quantified by comparing data from the pandemic and post-pandemic periods. As a strength of this study, we consider the fact that predictive models of mobility in the city of Zilina during the COVID-19 pandemic were created based on real data from sensors installed at intersections in the city, providing real information about vehicle numbers moving in the city. This dataset covers a sufficiently long period of the year, \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & **cars\_in** & **vans\_in** & **trucks\_in** & **unknown\_in** & **cars\_out** & **vans\_out** & **trucks\_out** & **unknown\_out** \\ \hline CART & 0.87 & 0.86 & 0.7 & 0.83 & 0.87 & 0.86 & 0.78 & 0.72 \\ CHAID & 0.87 & 0.87 & 0.71 & 0.80 & 0.87 & 0.86 & 0.77 & 0.75 \\ Nearest neighbour & 0.41 & 0.45 & 0.39 & NA & NA & NA & NA & NA \\ Regression & 0.52 & 0.56 & 0.50 & 0.57 & 0.58 & 0.58 & 0.60 & 0.54 \\ MLP neural network & 0.93 & 0.92 & 0.73 & 0.88 & 0.93 & 0.91 & 0.80 & 0.80 \\ MLP neural network & & & & & & & & \\ comprehensive model & 0.90 & 0.90 & 0.70 & 0.86 & 0.91 & 0.89 & 0.78 & 0.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Evaluation statistics of comparison models. allowing us to capture changes in mobility not only throughout the day but also across different periods of the year. These data were thoroughly checked, and all errors were identified and corrected. The erroneous data were corrected using a sophisticated statistical approach through regression modelling to preserve the most accurate information about mobility in the city. Another strength of the study is the use of modern machine learning methods, which achieve high accuracy in their predictions. Moreover, the models were created using the boosting method to enhance their predictive ability. The study not only describes the predictions obtained by the model but also interprets the impacts of the most important input variables used for mobility modelling. This demonstrated the impact of the pandemic and the implemented anti-pandemic measures on mobility in the city. However, this study also has its weaknesses. One can be identified that the COVID-19 pandemic is quantified using variables that are admittedly internationally recognised and published for all countries worldwide for comparability purposes. However, on the other hand, they capture the nationwide situation. They are not region-specific, which introduces a certain bias in modelling their relationship with the vehicle numbers in the city. It would be more precise to use variables that accurately reflect the situation during the pandemic, specifically in the individual city. However, mainly due to the unavailability of such data, we opted to use these variables. Another limitation of the study is the inability to compare traffic patterns with the pre-pandemic period up to 2019. Data from this period are not available because the WSN was not yet installed at that time.