Citation

Ting, Choo Yee and Tan, Nicholas Yu Zhe and Hashim, Hanis Hizal and Ho, Chiung Ching and Shabadin, Akmalia (2020) Malaysian Road Accident Severity: Variables and Predictive Models. In: Computational Science and Technology. Lecture Notes in Electrical Engineering (Computational Science and Technology), 603 . Springer Verlag, pp. 699-708. ISBN 9789811500572

Text 10.1007@978-981-15-0058-9.pdf - Published Version Restricted to Repository staff only Download (46MB)

Abstract

Road accident refers to an incident where at least one land vehicle with one or more people injured or killed. While there are many variables attributed to road accident, ranging from human to environmental factors, the work presented in this paper focused only on identifying predictors that could potentially lead to fatality. In this study, the raw dataset obtained from the Malaysian Institute of Road Safety Research (MIROS) was firstly preprocessed and subsequently transformed into analytical dataset by removing missing values and outliers. Such transformation, however, resort to large feature space. To overcome such challenge, feature selection algorithms were employed before constructing predictive models. Empirical study revealed that there were 26 important predictors for predicting accident fatality and the top five variables are month, speed limit, collision type, vehicle model and vehicle movement. In this work, six predictive models constructed were Random Forest, XGBoost, CART, Neural Net, Naive Bayes and SVM; with Random Forest outperformed the rest with an accuracy of 95.46%.

Item Type:	Book Section
Uncontrolled Keywords:	Accidents—Prevention, Accident severity, Optimal feature set, Predictive model
Subjects:	H Social Sciences > HV Social pathology. Social and public welfare. Criminology > HV675-677 Accidents. Prevention of accidents
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	16 Dec 2020 11:40
Last Modified:	16 Dec 2020 11:40
URII:	http://shdl.mmu.edu.my/id/eprint/7954

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