TY - GEN
T1 - Malaysian Road Accident Severity
T2 - Variables and Predictive Models
AU - Ting, C.-Y.
AU - Tan, N.Y.-Z.
AU - Hashim, H.H.
AU - Ho, C.C.
AU - Shabadin, A.
N1 - Publisher Copyright:
© 2020, Springer Nature Singapore Pte Ltd.
PY - 2020
Y1 - 2020
N2 - 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%.
AB - 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%.
KW - Accident severity
KW - Optimal feature set
KW - Predictive model
UR - http://www.scopus.com/inward/record.url?scp=85072953843&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/75f12d87-5349-3839-a137-f80030ed00f3/
U2 - 10.1007/978-981-15-0058-9_67
DO - 10.1007/978-981-15-0058-9_67
M3 - Conference contribution
SN - 9789811500572
T3 - Lecture Notes in Electrical Engineering
SP - 699
EP - 708
BT - Computational Science and Technology - 6th ICCST 2019
A2 - Alfred, Rayner
A2 - Lim, Yuto
A2 - Haviluddin, Haviluddin
A2 - On, Chin Kim
ER -
