# Comparative Study of Machine Learning Classifiers for Modelling Road Traffic Accidents
## 6 Conclusions  
This study investigated the strength of widely used ML classifiers for road traffic accident problems. The classifiers were NB, k-NN, AdaBoost, SVM, LR, and NB. These were evaluated with four missing data methods: MICE, k-NN, mean and median, and two-dimensionality reduction methods: PCA and LDA. Accuracy, RMSE, precision, recall, and the ROC curve (AUC) were the five performance metrics used to evaluate the ML models. The overall results revealed that RF performed marginally better across the experiments in terms of accuracy, precision, recall, and ROC (AUC) when compared with the other classifiers. RF had the lowest RMSE compared with all the other classifiers, indicating a better fit for the RF model. Overall findings of the study on this particular RTA dataset are as follows:  
1. Statistical analysis included two-dimensionality reduction methods, with LDA obtaining promising results compared with PCA. In terms of missing data methods, MICE achieved good results;
2. A wide range of ML methods was applied due to their popularity and characteristics. It was observed from the empirical analysis that RF performed best when compared with the rest;
3. Furthermore, the AUC evaluation method was introduced to validate the classification results once the evaluation performance was assessed using accuracy, precision, and recall.  
Some apparent limitations of the study are as follows: only a dataset from Gauteng province was utilised during the comparative analysis. The dataset contained a certain number of features for the specific area of interest, excluding other features that could have been beneficial to improving the model's performance. The data only contained four events/targets for possible scenarios to incorporate subclasses into the data.  
For future work, further hyperparameter tuning could improve the SVC model results because the classifier is more strongly influenced by proper parameter tuning. Future research could test other ML classifiers such as artificial neural networks and deep learning, testing similar methods with different datasets or provincial metro and expanding the data in years. The approach itself is a contribution that benefits RTA stakeholders such as model developers and researchers, and inform policymakers and transportation safety designers in terms of actions relating to modern traffic safety control and actual predictive models, which will help develop the field of transportation.  
T.B.: conceptualisation, data curation, formal analysis, methodology, experiments, writing--review and editing, and result validation. W.D. and B.S.P. project administration, supervision, conceptualisation, and critical revision. All authors have read and agreed to the published version of the manuscript.  
This research did not receive external funding.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data are not publicly available due to restrictions from the subject's agreement.  
The Department of Applied Information Systems, the Institute for Intelligent Systems, and the University of Johannesburg supported this research. The authors would also like to thank the Gauteng Department of Community Safety, South Africa, for permitting the use of the RTA dataset.  
The authors do not have any conflict of interest with other entities or researchers.