# The Use of Machine Learning Methods in Road Safety Research in Poland
## 1 Introduction
ML can be used to estimate the probability of traffic incidents, as well as to assess risks in specific locations and conditions [38; 39]. Predictions are typically based on historical data, such as collision or accident counts, participant injury levels, weather conditions, and traffic volumes [40]. These analyses are crucial for identifying high-risk areas, including hazardous road segments [41; 42]. This insight is invaluable for guiding preventive measures, such as redesigning infrastructure to improve safety. Another ML application in road safety involves systems equipped with cameras and sensors that automatically detect incidents, such as vehicle breakdowns, congestions, collisions, or infrastructure damage [43; 44]. By integrating ML algorithms, these systems can process large datasets efficiently, enabling faster responses to road incidents and, ultimately, improving safety outcomes [45]. ML is also employed to monitor and analyze driver behaviors, including acceleration, braking, and reactions to obstacles [46]. Algorithms can identify risky behavior patterns, such as driving while fatigued or distracted [47], which supports the continuous development of Advanced Driver Assistance Systems (ADAS) [48]. ML also plays a vital role in optimizing traffic flow, reducing the risk of collisions and accidents caused by congestion, sudden stops, or uneven traffic distribution [49]. Studies highlight the importance of ML in alleviating congestion, dynamically managing traffic signals, and even planning detours when routes become impassable [50; 51]. Research also demonstrates the potential of ML to predict the need for transport infrastructure repairs by analyzing data on its condition and environmental factors [9; 52]. This approach not only aids in making informed decisions about road and structural maintenance investments but also reduces the risk of traffic incidents caused by poor road conditions or excessive wear to infrastructure, such as bridge structures [53; 54]. However, less attention has been paid in the literature to developing mathematical models based on ML algorithms that identify traffic incidents in Poland and predict their consequences for participants. This gap has become the focus of this article. This publication aims to develop mathematical models based on ML algorithms to describe road safety in Poland. This study is based on two key research questions:  
* What factors significantly impact road safety in Poland? * How effective and efficient are classification models based on ML algorithms in describing road safety in Poland? To address these questions, this study first selected the most influential variables to reduce the data dimensionality and model size. This was followed by mathematical modeling of road safety in Poland using selected machine learning algorithms. The results not only identified factors that significantly affect the severity of injuries or the number of fatalities in accidents but, above all, also demonstrated the ability of ML-based mathematical models to predict threats and their consequences. This article is structured into several sections. The introduction presents a general outline of this study and provides a literature review, highlighting publications that examine the causes and consequences of road incidents and propose solutions to mitigate them. This section also outlines potential applications of ML in road safety research. The second section describes the materials used and the methods applied in this study. In the third section, the variables with the strongest impact on road safety are extracted and analyzed in detail. The fourth section focuses on mathematical modeling using selected ML algorithms and evaluates the quality of the proposed models. Then, the results obtained using all the considered models are discussed. Finally, this paper presents its conclusions, taking into account the limitations and directions of future research.