# Comparative Study of Machine Learning Classifiers for Modelling Road Traffic Accidents
###### Abstract  
Road traffic accidents (RTAs) are a major cause of injuries and fatalities worldwide. In recent years, there has been a growing global interest in analysing RTAs, specifically concerned with analysing and modelling accident data to better understand and assess the causes and effects of accidents. This study analysed the performance of widely used machine learning classifiers using a real-life RTA dataset from Gauteng, South Africa. The study aimed to assess prediction model designs for RTAs to assist transport authorities and policymakers. It considered classifiers such as naive Bayes, logistic regression, k-nearest neighbour, AdaBoost, support vector machine, random forest, and five missing data methods. These classifiers were evaluated using five evaluation metrics: accuracy, root-mean-square error, precision, recall, and receiver operating characteristic curves. Furthermore, the assessment involved parameter adjustment and incorporated dimensionality reduction techniques. The empirical results and analyses show that the RF classifier, combined with multiple imputations by chained equations, yielded the best performance when compared with the other combinations. machine learning; road traffic accidents; data analysis; missing data; dimensionality reduction 202222022022202  
accident can escalate traffic congestion, travel delays, and safety issues [3; 6; 7]. Secondary accidents may account for a lower ratio as compared with initial or primary accidents. However, despite this, they account for risks that are estimated to be six times greater than the initial accident, resulting in multiple traumas, serious and complex injuries, as well as overlapping injuries. The prevention of RTAs has become a priority in transportation management. Initial and secondary accidents can be identified and investigated using different data mining techniques that can help support transport authorities and contribute to reducing the high number of road injuries and fatalities [6; 8; 9; 10; 11]. Data mining techniques can be globally applied in road safety to improve life-threatening problems on the roads. Applications of data mining techniques in RTAs can help in the modelling and better understanding of RTA data records. These records contain important hidden patterns that RTA stakeholders and decision-makers can use to introduce better safety policies [12; 13]. Data mining techniques are available in data science and can be used to achieve numerous outcomes such as classification, prediction, outlier analysis, and clustering analysis. According to [12], data mining techniques are machine learning (ML) processes. ML is described as a method that can be used to make provisions for data analysis, decision making, and data preparation for real-life problems, and that allow self-learning for computers without any complex coding involved [14; 15]. Additionally, ref. [16] describes ML as an approach that focuses mainly on improving computer programs' capability of accessing data and using the data to learn for themselves. The learning begins with the data to look for any patterns in the dataset and make future decisions involving societal problems. ML methods are categorised as supervised, unsupervised, and semi-supervised. ML have successfully been implemented in automated stock trading, computer vision, health care, speech recognition, and customer services. This study investigated widely used supervised ML methods to perform comparative analysis using a real-life RTA dataset to present the best predictive model. The study used data collected from Gauteng province, South Africa. The study aimed to align with the Sustainable Development Goals (SDGs) document regarding road safety to reduce the high number of fatalities and injuries [17; 18]. The objectives of the study were as follows:  
1.