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Exploring the causes and effects of a hazardous event such as traffic accidents have been of vital importance to society. Statistical analyses have been widely implemented to understand and deduce inferences on the cause-effect analysis, and to anticipate the occurrences of accidents in the future. One of the issues that has not been solved through conventional statistical modelling is the existence of interrelationships between variables in the data set. However, with the advent of technology and the wide application of machine learning algorithm, this problem can be solved through the application of Bayesian network analysis, which is a directed acyclic probabilistic graphical model. By using Hill Climb (HC) and Tabu algorithms, the structure of the data was studied and the relationship was estimated through conditional probability, that is based on the Bayes' theorem. The results suggests that weather plays a major role in the increase of traffic accidents, and occurs by disrupting lighting conditions which then disrupts the traffic systems. Furthermore, the results indicate that fatal accidents have a higher likelihood to occur in head-on, turn over and out of control accidents. The use of the Bayesian network creates probability estimates to enable the identification of the risk and the necessary precaution needed to be implemented.

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