## 6 Using a Simulator to Produce Synthetic Data for Area Speed Control Systems
* Decel (maximum braking, m/s2): The maximum deceleration the driver can tolerate when braking. A higher value is a shorter braking distance and a more aggressive driving style. A lower value is careful braking, as in an inexperienced driver. * Length (vehicle length, m): Total length of the vehicle in meters. Defines the type of vehicle (e.g., 5 m is a passenger vehicle, 6.5 m a van). It affects traffic congestion, overtaking decisions, and blocking of intersections. * MinGap (minimum distance, m): The minimum distance a vehicle maintains from the preceding vehicle (regardless of speed). Smaller values represent a more approachable vehicle, i.e., one that is less prone to aggressive driving styles. Larger values represent safer driving. * MaxSpeed (maximum speed, m/s): The absolute maximum speed of a vehicle regardless of restrictions. It allows for simulation, for example, sports vehicles, buses, and emergency vehicles, which are assigned a specific maximum speed value. * SpeedFactor (average propensity to exceed limits): A factor that multiplies the speed limit (speedLimit) on a given stretch. Allows modeling the type of driver: cautious, normal, aggressive. * SpeedDev (population standard deviation, dimensionless value): Statistical variation in behavior in a population of drivers of a given type. A larger value translates into more variation in behavior. It also affects the random modification of speedFactor for each vehicle. * Sigma (random noise in driver decisions, from 0 to 1): Reflects the driver's level of \"imperfection\": whether he drives perfectly (0) or makes mistakes (e.g., 0.5). A larger value indicates greater randomness in decisions (e.g., maneuvers and reactions). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Type** & **Accel [m/s2]** & **Decel [m/s2]** & **Length** & **minGap** & **maxSpeed [m/s]** & **speedFactor** & **Speed** & **Sigma** & **Share [\%]** \\ \hline
**slow** & 2.2 & 3.5 & 5 & 3.2 & 13.89 & 1.0 & 0.10 & 0.30 & 10 \\
**correct** & 2.6 & 3.8 & 5 & 2.5 & 16.67 & 1.0 & 0.10 & 0.05 & 10 \\
**typical** & 2.6 & 4.0 & 5 & 2.5 & 22.22 & 1.0 & 0.15 & 0.10 & 50 \\
**fast** & 2.8 & 4.2 & 5 & 2.0 & 25.00 & 1.1 & 0.20 & 0.15 & 15 \\
**aggressive** & 3.2 & 4.5 & 5 & 1.8 & 27.78 & 1.1 & 0.25 & 0.30 & 5 \\
**bus driver** & 1.2 & 1.2 & 6.5 & 3.0 & 25.00 & 1.1 & 0.30 & 0.20 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Example driver types for one of the simulation scenarios. * Share (traffic share, %): Shows the percentage of traffic share of a driver of a given type. By modifying the data and parameters presented above, we obtain further simulation scenarios. These can reflect specific behaviors as well as particular times of day, weeks, or years. This is made possible by the appropriate manipulation of the dataset, guided by expert knowledge derived, at a minimum, from preliminary field research. Another source of information necessary to calibrate the simulation environment is, of course, the research results contained in the literature, conference publications, and research reports [56]. A series of simulations was carried out as part of the research, taking into account various traffic conditions. Each scenario differed in the proportions of vehicle and driver types (e.g., aggressive drivers), controlled via parameters such as speedFactor. Additionally, variations in traffic volume across different times of day were modeled. The General Traffic Survey report served as the basis for input calibration [56]. Four selected scenarios (sim 1, sim 2, sim 3, and sim 4) were presented in the results section, as they most closely corresponded to the observed field conditions.