# Comparative Study of Machine Learning Classifiers for Modelling Road Traffic Accidents
## 2 Literature Review
### Related Studies  
The related studies show an increasing interest in RTAs. This section reviews some prediction models which have incorporated traditional ML methods. The benefits of the various prediction models are taken into consideration. ML methods have been employed for road accident prediction using primary and secondary road accident datasets. The most considered among these methods are SVM, decision trees (J48 or C4.5), RF, least squares support vector machine (LSSVM) and LR. A study by [6] modelled the occurrence of secondary accidents using LSSVM and back-propagation neural networks (BPNNs); the investigation revealed that BPNNs performed best in terms of the correlation coefficient (CORR) and mean squared error (MSE). A study by [22] presented a comparative analysis of sequential minimal optimisation (SMO), J48, and instance-based learning with k-parameter methods. The study's findings discovered that the SMO algorithm accurately compared with the other methods. Another study [23] predicted traffic accident severity using supervised ML methods such as LR, NB, RF, and AdaBoost. The study considered the freeway crash dataset, with the RF performing best with high accuracy of 75.5%. Ref. [24] investigated road accident analysis and predicted accident severity by considering four supervised methods: k-NN, DT, AdaBoost, and NB. The results of the study revealed that AdaBoost outperformed the other methods. Furthermore, in [25; 26; 27], the authors investigated road accidents using real-life data considering methods such as J48, LSSVM, and RF. Other studies used probabilistic reasoning models such as Bayesian networks, or BNs, [28; 29; 30; 31; 32], with [28] performing a comparison between BNs and regression models. The study's results showed that BN achieved the best performance. Artificial neural networks (ANNs), BPNNs, and multilayer perceptron (MLP) methods were applied during road accident predictions [33; 34; 35; 31]. In [33], the authors used ML methods to compare models for incident duration prediction, obtaining promising results. Another study [35] presented road accident detection by comparing the performance of three methods: SVM, RF and ANN. The study showed that RF achieved the best results. Other studies [22; 24; 30; 32] considered k-NN and classification and regression trees (CARTs) to predict road accidents. It was observed from the literature that there are various reasons why a study uses a specific method. One such reason is the data type, which can be categorised into primary and secondary datasets. Studies that designed a predictive model using secondary road accident data were those of [6; 29]. A study by [34] considered using primary and secondary RTA datasets. In this study, MLP performed best during the modelling of the traffic risk of secondary incidents. Another study [36] proposed the importance of performing exploratory data analysis on the road traffic accident dataset. The authors revealed which features affect road accidents and their negative impact during the investigation. A study by [37] presented a method of modelling and characterising traffic flow. The study employed regressionand clustering methods, which achieved very promising results. A Bayesian network-based framework was developed for assessing the cost of road traffic accidents [38]. This study managed to identify which features can be incorporated into the framework to assess different negative impacts on road accidents. The framework presented promising results. Lastly, [39] presented a study using adaptive Kalman filtering to predict urban road networks. The study revealed that the proposed model is capable of predicting traffic correctly. Most ML classifiers are influenced by the size of the dataset and capabilities to handle overfitting problems and are being implemented in different environments such as urban and rural settings and on freeways and highways. Evidence from a study by [25] demonstrated promising results when the RF method was compared with other classifiers.