###### Abstract  
This work focuses on the possibilities of generating synthetic data for machine learning in hazard prediction in area-based speed monitoring systems. The purpose of the research conducted was to develop a methodology for generating realistic synthetic data to support the design of a continuous vehicle speed monitoring system to minimize the risk of traffic accidents caused by speeding. The SUMO traffic simulator was used to model driver behavior in the analyzed area and within a given road network. Data from OpenStreetMap and field measurements from over a dozen speed detectors were integrated. Preliminary tests were carried out to record vehicle speeds. Based on these data, several simulation scenarios were run and compared to real-world observations using average speed, the percentage of speed limit violations, root mean square error (RMSE), and percentage compliance. A new metric, the Combined Speed Accuracy Score (CSAS), has been introduced to assess the consistency of simulation results with real-world data. For this study, a basic hazard prediction model was developed using LoRaWAN sensor network data and environmental contextual variables, including time, weather, location, and accident history. The research results in a method for evaluating and selecting the simulation scenario that best represents reality and drivers' propensities to exceed speed limits. The results and findings demonstrate that it is possible to produce synthetic data with a level of agreement exceeding 90% with real data. Thus, it was shown that it is possible to generate synthetic data for machine learning in hazard prediction for area-based speed control systems using traffic simulators.  
arc:27 June 2025  
29 July 2025  
30 July 2025  
31 July 2025  
**Citation:** [PERSON]; [PERSON] Synthetic Data Generation for Machine Learning-Based Hazard Prediction in Area-Based Speed Control Systems.  
_Article_  
**M.**: _A._: _A.  
multiple points dispersed in the area, allow the concept of area-based speed control to be realized. Such systems allow not only the detection of individual speeding incidents but also the continuous monitoring of traffic patterns and the identification of areas particularly at risk of traffic incidents. Parallel to the development of the measurement infrastructure, we are seeing intensive progress in data analysis and artificial intelligence. Machine learning methods are finding increasing application in traffic hazard prediction, enabling the creation of models that can predict risky situations based on historical data and current observations. High-quality input data is a prerequisite for the effectiveness of predictive models used in intelligent transport systems (ITSs). However, obtaining such data in real-world conditions can be costly, time-consuming, and often limited by legal regulations, especially in the area of privacy protection. An additional barrier is the difficulty of recording rare, extreme, or dangerous traffic scenarios. In many cases, especially when a solution is still at the conceptual stage, it may be impossible to collect complete data. In such situations, synthetic data becomes particularly important, as it can be used both to train machine learning models and to assess the effectiveness of proposed solutions preliminarily. The most commonly used methods for generating such data include advanced traffic simulations (e.g., using tools such as SUMO) and artificial intelligence-based approaches, including generative models such as GAN (Generative Adversarial Networks) and VAE (Variational Autoencoders) [3,4]. The effectiveness of these approaches ultimately depends on the quality of the generated data and how accurately they reflect real-world behavior and traffic conditions.