# Analysing Urban Traffic Patterns with Neural Networks and COVID-19 Response Data
## 3 Methodology and Data
### Methodology of the Study  
This study aimed to create a predictive supervised model using a neural network that could accurately predict the number of vehicles of different types entering and leaving the city centre at each monitored sensor. To achieve this goal, we approached the task in this study as a data mining project, utilising the CRISP-DM methodology (Cross Industry Standard Process for Data Mining, publicly available on the CRISP-DM website [50]). This methodology involves dividing the data mining task into six main phases outlined in the following diagram, see Figure 5.  
As we consider creating a predictive model for vehicle numbers as a data mining task, the dataset is the basic source of information for solving the task. Since this involves a  
Figure 5: CRISP-DM methodology. Source: [50].  
large volume of automatically collected data that cannot be processed using conventional methods, we consider it big data. Subsequently, the data mining project is divided into the following six phases [50]:  
1. _Business (problem) understanding_: In this phase, we define the problem to be solved very specifically. We define the solution vision, criteria, and values the solution should meet. We try to understand the ongoing processes captured in the data.
2. _Data understanding_: In this phase, we work with the data, beginning by identifying suitable data sources, verifying the existence of appropriate data for solving the problem, and their availability. The data used in this study are described in the next section.
3. _Data preparation_: In this phase, we integrate data from various sources and databases and then proceed with their preparation until we prepare them into a modelling matrix for creating the predictive model. These processes include cleaning, validating values, calculating new variables, aggregation, filtering, and similar data operations.
4. _Modelling_: We apply machine learning methods to find the most suitable method applicable to the given data for solving the defined problem. The output of this phase is the created predictive model.
5. _Evaluation_: We check the created solution and assess its quality using evaluation statistical metrics. We verify the fulfilment of the criteria defined for the solution in the first phase.
6. _Deployment_: The final phase of the data mining project is deploying the created model into practice and implementing it into the enterprise's internal systems or other task issuers.  
To predict mobility during the COVID-19 pandemic, we used the statistical software IBM SPSS Statistics (version 26) for the data preparation phase and the data mining software IBM SPSS Modeler (version 18.3) for the modelling and evaluation phases. IBM SPSS Modeler operates on the principle of visual programming, and its significant advantage lies in its data openness. Firstly, the created modelling streams can be utilised for newly emerging or newly acquired datasets, thereby gaining new predictions. Additionally, the created models can be adapted to changed circumstances. If, over time, we find that the predictions generated by the model are no longer sufficiently accurate and do not meet the established qualitative criteria, or if the circumstances of mobility in the city have changed to the extent that it would be appropriate to capture the new situation using data in the model, the models can be updated by retraining them on new data.