## 4 Use of Traffic Simulators to Assess Drivers' Propensity to Exceed Speed Limits  
Traffic simulators are now an indispensable tool in transportation engineering and urban planning. They enable the quick, inexpensive, and accurate reproduction of complex traffic phenomena in a virtual environment. Such methods enable the analysis of scenarios that are otherwise difficult or impossible to reproduce in practice due to cost, safety concerns, regulatory constraints, or a lack of real-world data. This makes them indispensable in studies of the effectiveness of traffic management systems, the impact of infrastructure, or the behavior of traffic participants. Among the most advanced tools of this type are SUMO [44], PTV VISSIM [45], Aimsun Next [46], and MATSim [47]. Each offers a different level of detail and specificity of approach. SUMO, an open-source project, offers extensive customization and integration with external applications, making it highly flexible and valuable for both academic research and implementation projects [48]. VISSIM, on the other hand, is distinguished by its high level of realism in replicating the road environment and driver behavior, making it ideal for assessing the impact of implementing autonomous vehicles and advanced driver assistance systems. A different approach is presented by MATSim, which, instead of modeling individual vehicles, focuses on simulating the 24 h activity of agents. This approach enables traffic to be analyzed on a macro scale, which is particularly useful in strategic planning and the evaluation of transportation policies [49]. Regardless of the chosen platform, realistic modeling of driver behavior is crucial in simulations, as it largely determines the results of the analysis. Failure to account for critical psychophysical aspects, such as fatigue, aggression, distraction, or susceptibility to external conditions, can lead to erroneous conclusions and, thus, suboptimal management  
Figure 6: Measurement results for the SD 07 detector, 50 km/h speed limit (source: own study). solutions [50]. Microsimulation models, such as those implemented in SUMO, enable the tracking of decisions made by individual vehicles, including reactions to other objects, lane choice, speed adjustments, and distance adjustments, among others [51]. SUMO offers diverse models of vehicle following (car-following), including the Krauss model and an extended version of the Intelligent Driver Model (IDM) [52], which can be further calibrated according to geographic context and road type. Particular importance is now attributed to research on speeding propensities, as speeding is one of the main risk factors in traffic [53]. Thanks to simulations, it is possible not only to identify problem areas but also to address them effectively. Also, an evaluation of the effectiveness of preventive measures, from police control to smart speed limits. Models also enable the analysis of the impact of various conditions, such as road geometry, weather conditions, or traffic volume, on drivers' decisions and the risk of collisions. In practice, simulations are used in numerous case studies and optimization projects. Among other things, the effects of autonomous vehicles on traffic flow at intersections, the effectiveness of variable speed limits in reducing emissions, and the efficiency of converting classic traffic circles into turbine traffic circles are analyzed. SUMO also finds applications in evaluating real-world traffic scenarios. These include, in particular, the calibration of traffic signals, evaluation of intersection capacity, or forecasting the effects of legislative changes, such as raising the speed limit for trucks [54]. Despite many successes, simulators still face significant limitations. These include oversimplified behavioral models, difficulties in validation against real data, and differences in results between different simulation environments.