# Analysing Urban Traffic Patterns with Neural Networks and COVID-19 Response Data
## 4 Results  
### Results of Data Preparation  
In the Methodology of Data Preparation section, we emphasised the importance of thorough verification and adjustment of vehicle numbers reported by sensors, especially in the case of inaccurately reported values during sensor failures. For this purpose, we developed individual regression models that helped us correct erroneous values. These were further adjusted to ensure the consistency of cumulative vehicle counts after the sensor connection interruption was resolved. Additionally, we had to address some unacceptably high values, which were corrected by replacing them with values predicted from linear regression models. Table 4 presents the results of corrections compared to the actual values for sensor 1. The main statistical characteristics of the actual and corrected values for all four types of vehicles and both directions are provided. At the end of the table, the absolute and relative differences between the actual vehicle numbers and the numbers corrected by the proposed method are quantified. The last three rows in each sensor's table (both for \(in\) and \(out\) directions) demonstrate that the proposed procedure for correcting values during sensor communication failures accurately replaced the error values. The percentage errors of these corrections, caused by rounding the estimated vehicle numbers to integer values, are quantified in the last row of the table. Some sensors (e.g., sensor 6 in the direction \"in\" or sensor 9) exhibit higher correction errors because, apart from failures, these sensors also had erroneously reported values that were not caused by communication failures. These erroneous values manifested as reporting an unreasonably high number of vehicles in a given 15-min interval. Since the source of this error could not be identified, these values were only replaced with predicted values from the created regression models, and they could not be corrected using the correction procedure mentioned above, as the correct value is unknown. This resulted in a higher error in the sum of corrected values compared to the total actual number of vehicles of a particular type for the entire observation period. The data, adjusted in this way and prepared into the final form of the modelling matrix, can also be described using a graphical representation. The number of vehicles of different types in the direction toward  
Figure 9 illustrates the vehicle numbers in the direction from the city centre (direction _out_), colour-coded according to the individual sensors. The total number of vehicles in the inward and outward directions is depicted using boxplots in Figure 10. The boxplots are differentiated by individual sensors and represent statistical characteristics: the inner line in the box is the median vehicle number, and the upper and lower edges of the box are the upper and lower quartiles of the vehicle number. Additionally, the boxplot highlights outliers (circles) and extreme values (crosses), which are vehicle numbers that differ from the typical numbers. These correspond to traffic peaks and can indicate which sensors typically record such high vehicle numbers. The following figures describe the relationships between selected two COVID-19 pandemic-related variables and the number of vehicles. Figure 11 depicts the boxplots showing the relationship between school closing and the number of vehicles of different types. On the left side are the boxplots for direction in, and on the right side the direction out. The scale of the vertical axis has been adjusted for better visibility of the range of values; therefore, not all values marked as outliers are visible. The exact values of statistical characteristics shown in Figure 11 are listed in Table 5. The median values show that the level of school closing measures impacted the number of vehicles. A similar dependence is depicted in Figure 12 for the numbers of vehicles and workspace closures measure levels.