### Literature Review  
RTAs are a significant public health concern, influenced by a complex interplay of factors. Various studies emphasise the need for a multidimensional approach to understand and prevent RTAs. One such study reviewed various data sources and techniques for accident analysis, emphasising the benefits of combining multiple analytical methods [6]. Another study employed system dynamics to model the complexity of RTAs, highlighting the importance of considering non-linear interactions between variables [7]. A multidimensional and multi-period analysis of road safety, incorporating various criteria such as human factors, accident causes, and road characteristics, was proposed in previous research [8]. The multifactorial nature of accidents involving human, vehicular, and environmental elements has also been reviewed in several studies [9]. Furthermore, black spot identification methods that couple statistical analysis with accident severity indices have been discussed as a more reliable approach for road safety assessments [10]. Traditional methods, such as generalized logistic regression and classification trees, have been widely used to identify combinations of factors leading to fatal accidents [11]. Researchers have applied association rule mining to reveal complex interactions between human, vehicle, road, and environmental factors in multi-fatality crashes [12]. A novel matched crash vs. non-crash approach for analysing severe crash patterns on multilane highways has also been introduced [13]. Logistic regression models have been developed to estimate fatality and major injury probabilities in single-vehicle accidents, with the major injury model showing better explanatory power [14]. More recent advancements in machine learning have introduced techniques that enhance the precision and scope of RTA analysis. Machine learning methods such as random forests, support vector machines (SVM), and deep learning models are now being applied to traffic accident data to capture non-linear and complex relationships between variables. For instance, random forests have proven effective for accident severity prediction by leveraging ensemble learning, which boosts accuracy by combining multiple decision trees [14]. Similarly, SVMs have been useful for classifying accident severity outcomes by optimizing the margin between different classes for improved classification accuracy [15]. Deep learning approaches, such as neural net works, have shown promise in real-time prediction of accident severity and in identifying complex patterns that are less apparent through traditional methods [16]. In addition, unsupervised learning methods, like clustering and dimensionality reduction techniques, including principal component analysis (PCA), are being increasingly used to explore latent structures within complex accident datasets [17]. These methods complement traditional approaches and provide a more detailed understanding of accident dynamics, enabling better accident prediction and risk factor identification. When analysing RTA data, one of the key objectives is to quantify the influence of accident-related variables on the severity of injuries. Several studies have identified factors that influence both accident severity and frequency. Critical factors such as collision type, road configuration, vehicle type, driver characteristics, and environmental conditions have been highlighted in research [15; 16]. Specifically, motorcycles, male drivers, elderly drivers, nighttime driving, high-speed roads, and unlit roads have been pointed out as significant risk factors associated with higher accident severity [16]. Furthermore, studies have examined the relationship between safety devices and injury outcomes. For instance, safety devices, narrow impact zones, ejection, airbag deployment, and higher speeds have been strongly correlated with more severe injuries [17].