Decision Tree Model for Non-Fatal Road Accident Injury

Fatin Ellisya Sapri, Nur Shuhada Nordin, Siti Maisarah Hasan, Wan Fairos Wan Yaacob, Syerina Azlin Md Nasir


Non-fatal road accident injury has become a great concern as it is associated with injury and sometimes leads to the disability of the victims. Hence, this study aims to develop a model that explains the factors that contribute to non-fatal road accident injury severity. A sample data of 350 non-fatal road accident cases of the year 2016 were obtained from Kota Bharu District Police Headquarters, Kelantan. The explanatory variables include road geometry, collision type, accident time, accident causes, vehicle type, age, airbag, and gender. The predictive data mining techniques of decision tree model and multinomial logistic regression were used to model non-fatal road accident injury severity. Based on accuracy rate, decision tree with CART algorithm was found to be more accurate as compared to the logistic regression model. The factors that significantly contribute to non-fatal traffic crashes injury severity are accident cause, road geometry, vehicle type, age and collision type.


road accident injury severity; logistic regression; decision tree; CART; LR main

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Published by INSIGHT - Indonesian Society for Knowledge and Human Development