# The Use of Machine Learning Methods in Road Safety Research in Poland
## 1 Introduction  
Road safety is one of the most critical challenges facing modern society, significantly impacting public health, the economy, and the overall quality of life [1]. Each year in Poland, thousands of traffic incidents result in fatalities and severe injuries among road users. Reports from the Polish National Police Headquarters and other safety analysis institutions indicate that despite preventive measures, the number of such incidents remains alarmingly high [2, 3]. In this context, implementing advanced solutions to enhance the road safety analysis and management process is essential [4]. One of the most rapidly evolving technological tools in this domain is machine learning (ML), which can play a pivotal role. ML enables the analysis of large and complex datasets that traditional methods often struggle to process efficiently [5, 6]. In the context of road safety, ML can identify patterns and correlations between factors contributing to traffic incidents and even predict potential risks and their consequences, such as injuries or fatalities. Globally, ML applications in road safety include developing intelligent traffic management systems, identifying high-risk road segments, analyzing driver behavior, and forecasting infrastructure failures [7, 8, 9]. In Poland, the use of ML in road safety is particularly relevant due to the rapid expansion of the transportation network and ongoing urbanization [10, 11]. Additionally, the structure of Poland's road network, dominated by local roads where the risk of incidentsis significantly higher than on expressways, underscores the importance of innovative approaches [12]. Currently, most road safety research in Poland focuses on the causes of road incidents and their consequences for participants [13; 14]. The authors of these publications conduct analyses of the injured and casualties by year, while making comparisons against other EU Member States and pointing out changing trends [15; 16; 17]. The causes of collisions and accidents in road traffic are equally often addressed [18]. The relationship between weather conditions and the number of incidents is mainly analyzed [19; 20]. Other studies emphasize that in addition to the weather, driver behavior and traffic volume, which determines, among other things, the increase in the total number of vehicles on the road, have an impact on the increase in incidents [21]. In addition, the number of accidents has been shown to depend on the time of day [22]. Due to the multitude of factors that determine the occurrence of accidents in Poland, a methodology for optimizing them is proposed [23]. Publications often point out potential solutions and areas for improvement to enhance the safety of road users [24; 25]. Advanced Driver Assistance Systems (ADAS) and the development of autonomous technologies are frequently highlighted as promising approaches to reducing collisions and accidents [26; 27]. An extremely popular issue in recent years has been the \"Vision Zero\" strategy of using all the available methods and means to almost completely minimize injuries and casualties [28]. It is pointed out that complementary to the achievement of this goal can be road infrastructure development, legislative action by the authorities, international cooperation, and the implementation of innovative technologies in vehicles, among others [29; 30]. Mathematical modeling is a widely used tool in road safety research, particularly for evaluating current safety levels and developing effective solutions [31; 32]. It is also applied to assess the reliability of transportation systems [33; 34; 35]. Recently, the significance of machine learning (ML) tools in this area has grown substantially, as evidenced by numerous applications [36]. However, fully harnessing the potential of ML algorithms requires a unified system for collecting and sharing data on road incidents and traffic volumes [37]. Establishing such a system should be a priority for policymakers aiming to improve road safety.