## 6 Using a Simulator to Produce Synthetic Data for Area Speed Control Systems  
Synthetic data in machine learning are artificially generated data. They are not based on actual measurements or observations but rather are created using algorithms, statistical models, or computer simulations. Their primary purpose is to support the processes of training, testing, and validating models, especially in situations where real data is challenging to obtain, incomplete, expensive, or subject to legal restrictions. This exemplifies the type of challenge encountered at the conceptual stage of an area-based vehicle speed control system. In general, synthetic data can be created in several ways, including the simulation of physical phenomena, the random generation of data according to specific distributions, the transformation of real data, or the use of generative machine learning models such as GAN (Generative Adversarial Networks) or VAE (Variational Autoencoders) [54]. They have the advantage of complete control over the structure and labels of the data, as well as the ability to generate large, diverse sets in a short period. Properly acquired synthetic data can faithfully reproduce real-world features and significantly improve the performance of models, especially in cases where real-world data is unavailable or insufficient. As mentioned earlier, simulators such as SUMO are primarily used for modeling and optimizing vehicle flow, planning road infrastructure, evaluating the effectiveness of traffic signals, testing traffic organization in urban and non-urban conditions, and predicting the impact of traffic investments on the quality of transportation services. With advanced configuration options, the ability to edit routes and traffic parameters, and the creation of custom profiles (e.g., drivers) and custom Python 3.11 scripts, however, the ultimate scope of their use is much greater. Sometimes, these scenarios are far beyond the scope of classic traffic analysis, such as simulations on the impact of deploying electric car charging stations on the distribution network [55]. The use of SUMO to produce synthetic data for vehicle speed analysis is, therefore, not a standard approach. However, it is based on the general architecture of SUMO and, in general terms, does not differ significantly from its other applications (as shown in Figure 7). It will require the creation of a dataset on the distribution of speed detectors in the simulated road network. Additionally, it will require the creation of models (profiles) of the vehicles' drivers involved in the simulation. Preparation of the simulation environment begins with the acquisition of road network data in OSM format, which is sourced from OpenStreetMap (specifically, the gmina_stare_babice.osm file). This network is shown in Figure 8. It is then processed using the netconvert.exe tool, which generates the actual road network in SUMO format as the gmina_stare_babice.net.xml file. If necessary, we also use the netedit.exe editor to correct or expand the network structure manually. The editor is also helpful for checking the placement of speed detectors or identifying a road section (at the edge of the network). The next step is to generate data on vehicle travel routes. To achieve this, we utilize the randomTrips.py tool, which enables us to generate realistically distributed traffic over a specified time interval. The generated data is saved in a file move.rou.xml. The data undergoes additional processing and is filtered using the clear_routes.py script to remove erroneous trips that may have been included in the data. Next, the assign_types.py script is run, which assigns one of the predefined driver types to each vehicle while maintaining the assumed percentage. These types are specified in the vehicle_types.add.xml file, which contains detailed parameters such as maximum speed, acceleration, congestion behavior, and safety margins. Another key element of the simulation is the ancillary infrastructure included in the speed_detectors.add.xml file.