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%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
World Health Organization, http://www.who.int/mediacentre/factsheets/fs358/en/, last accessed 2017.
Jiang, C., Lu, L., Chen, S., & Lu, J. J.: Hit-and-run crashes in urban river-crossing road tunnels. Accident Analysis & Prevention, 95, 373–380 (2016).
Phillips, R. O., & Sagberg, F.: Road accidents caused by sleepy drivers: Update of a norwegian survey. Accident Analysis & Prevention, 50, 138–146 (2013).
Xie, W., Wang, J., & Ragland, D. R.: Utilizing the eigenvectors of freeway loop data spatiotemporal schematic for real time crash prediction. Accident Analysis & Prevention, 94, 59–64 (2016).
World Health Organization: Global status report on road safety 2015, (2015).
Institute for Health Metrics and Evaluation, http://www.healthdata.org/Malaysia, last accessed 2017.
Iranitalab, A., & Khattak, A.: Comparison of four statistical and machine learn-ing methods for crash severity prediction. Accident Analysis & Prevention, 108, 27-36 (2017).
Jeong, H., Jang, Y., Bowman, P. J., & Masoud, N.: Classification of motor ve-hicle crash injury severity: a hybrid approach for imbalanced data. Accident Analysis & Prevention, 120, 250-261 (2018).
Ward, N., Linkenbach, J., Keller, S., & Otto, J.: Toward Zero Deaths: A National Strategy on Highway Safety. Western Transportation Institute, Bozeman (2010).
Garrido, R., Bastos, A., de Almeida, A., & Elvas, J. P.: Prediction of road accident severity using the ordered probit model. Transportation Research Procedia, 3, 214–223 (2014).
Xu, J., Kockelman, K. M., & Wang, Y.: Modeling crash and fatality counts along mainlanes and 1 frontage roads across texas: 2 the roles of design, the built environment, and weather 3. In Presented at the 93rd Annual Meeting of the Transportation Research (p. 24). volume 22 (2014).
Fernandez-Delgado, M., Cernadas, E., Barro, S., & Amorim, D.: Do we need hundreds of classifiers to solve real world classification problems. J. Mach. Learn. Res, 15, 3133–3181 (2014).
Goh, K., Currie, G., Sarvi, M., & Logan, D.: Factors affecting the probability of bus drivers being at-fault in bus-involved accidents. Accident Analysis & Preven-tion, 66, 20–26 (2014).
George, Y., Athanasios, T., & George, P.: Investigation of road accident severity per vehicle type. Transportation research procedia, 25, 2076–2083 (2017).
Azadeh, A., Zarrin, M., & Hamid, M.: A novel framework for improvement of road accidents considering decision-making styles of drivers in a large metropolitan area. Accident Analysis & Prevention, 87, 17–33 (2016).
Celik, A. K., & Oktay, E.: A multinomial logit analysis of risk factors influencing road traffic injury severities in the erzurum and kars provinces of turkey. Accident Analysis & Prevention, 72, 66–77 (2014).
Goel, G., & Sachdeva, S.: Analysis of road accidents on nh-1 between rd 98 km to 148 km. Perspectives in Science, 8, 392–394 (2016).
Fernandes, A., & Neves, J.: An approach to accidents modeling based on com-pounds road environments. Accident Analysis & Prevention, 53, 39–45 (2013).
Theofilatos, A., Yannis, G., Kopelias, P., & Papadimitriou, F.: Predicting road accidents: a rare-events modeling approach. Transportation Research Procedia, 14, 3399–3405 (2016).
Theofilatos, A.: Incorporating real-time traffic and weather data to explore road accident likelihood and severity in urban arterials. Journal of safety research, 61, 9–21 (2017).
Prieto, F., Gómez-Déniz, E., & Sarabia, J. M.: Modelling road accident black-spots data with the discrete generalized pareto distribution. Accident Analysis & Prevention, 71, 38–49 (2014).
Kumar, V., & Minz, S.: Feature selection. SmartCR, 4, 211–229 (2014).
Tang, J., Alelyani, S., & Liu, H.: Feature selection for classification: A review. Data Classification: Algorithms and Applications, (p. 37) (2014).
Lloyd, L. K., & Forster, J. J.: Modelling trends in road accident frequency bayesian inference for rates with uncertain exposure. Computational Statistics & Data Analysis, 73, 189–204 (2014).
Gitelman, V., Doveh, E., & Bekhor, S.: The relationship between free-flow travel speeds, infrastructure characteristics and accidents, on single-carriageway roads. Transportation Research Procedia, 25, 2026–2043 (2017).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ting, CY., Tan, N.YZ., Hashim, H.H., Ho, C.C., Shabadin, A. (2020). Malaysian Road Accident Severity: Variables and Predictive Models. In: Alfred, R., Lim, Y., Haviluddin, H., On, C. (eds) Computational Science and Technology. Lecture Notes in Electrical Engineering, vol 603. Springer, Singapore. https://doi.org/10.1007/978-981-15-0058-9_67
Download citation
DOI: https://doi.org/10.1007/978-981-15-0058-9_67
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0057-2
Online ISBN: 978-981-15-0058-9
eBook Packages: EngineeringEngineering (R0)