# Analysing Urban Traffic Patterns with Neural Networks and COVID-19 Response Data
## 3 Methodology and Data  
### Data Used in the Study  
The study is conducted using real data from sensors installed at intersections in the city of Zilina. The data are publicly available from the website [[https://dashboards.clevernet.sk](https://dashboards.clevernet.sk)]([https://dashboards.clevernet.sk](https://dashboards.clevernet.sk)) (accessed on 1 February 2023), where, among other information, the numbers of vehicles passing through individual installed sensors are provided. Nine areas along significant streets in the city are monitored, all in both inbound (toward the centre) and outbound (away from the centre) directions [47]. The list of monitored streets and the number of sensors is detailed in Table 1.  
In this study, we calculated the total vehicle numbers at each sensor within an intersection for simplification. Therefore, in the following text, when we refer to a \"sensor\", we mean the cumulative number of vehicles, and we will denote these sensors by the numbers assigned to them in the first column of Table 1. The location of the sensors within the city is illustrated in Figure 3.  
In the study, we utilised data from 6 June 2021 to 5 August 2022. This timeframe was chosen to ensure that all sensors were already operational, as some were installed later or as additional units, ensuring comparability of analyses. The end date of the analysed period was selected as the most recent date for which data were available when the analysis commenced. The number of vehicles passing each sensor is monitored in 5-min intervals. For this study, we aggregated vehicle numbers into 15-min time intervals, resulting in a total of 365,400 records for the observation period. Vehicles passing through the sensors are categorised into four groups: cars, vans, trucks, and unknown. The type of vehicle is automatically determined by the sensor based on its dimensions. The types of distinguished vehicles are illustrated in Figure 4. \begin{table}
\begin{tabular}{c c} \hline \hline
**Street Code in the Study** & **Number of Sensors** \\ \hline
3 & 2 \\ \hline
4 & 2 \\ \hline
5 & 2 \\ \hline
6 & 4 \\ \hline
7 & 2 \\ \hline
8 & 5 \\ \hline
9 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Number of sensors in the city. Figure 3: Location of sensors within the city. Additionally, speeds at which vehicles pass through the sensors are recorded, distinguishing speeds into categories of up to 30 km/h, between 30 and 60 km/h, and above 60 km/h. The lane through which the vehicle passes (left, middle, right, if such lanes exist at the given intersection) is also recorded. However, for this study, we did not differentiate between vehicle speeds or lanes. Therefore, vehicle numbers were only distinguished by vehicle type and direction, aggregated across all speeds and lanes. Therefore, the entire database for this analysis is composed of records from 9 sensors, each in two directions (in and out), for each of the four types of vehicles (cars, vans, trucks, unknown). Therefore, in total, the database contains 72 variables, which we consider dependent (target) variables for this study. The list of target variables and their labels used in the study is in Table 2.  
In this study, we focus on creating a mobility model, specifically the model for predicting the number of vehicles moving at these key intersections in Zilina. The model is constructed using the method of neural networks, which is explained in more detail in the methodological section. The input variables for this model include variables whose names and explanations are provided in Table 3.