Road fatalities are the number of deaths caused by road accident intentionally or unintentionally. Previous studies show that the number of road fatalities in Malaysia is still concerning and increasing from year to year. This unresolved issue was not only life threatening but it also potentially gives major problems to the economic growth in the country. This study aims to determine the best model in forecasting road accident fatalities in Malaysia. There are two methods used which are Autoregressive Integrated Moving Average (ARIMA) model and Auto-Regressive Poisson model. Each of the model was constructed and analysed by using Statistical Package for Social Science (SPSS) and R Studio, then the models were evaluated and compared to each other based on their error value. The error measure that used in this study are Relative Fit Error (RFE), Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) by selecting the lowest value of error measure. The lowest error measure considered as the best model and will be used to forecasts the number of road deaths in five years ahead that is 2021 until 2025. The results indicate that the best model is ARIMA (0,2,1) from the Box-Jenkins method as it has the lowest error measure as compared to Auto-Regressive Poisson model. On this basis, it is recommended that future research to implement ARIMA model in forecasting and to include other type of model to be compared with in measuring the error measure.
Al-Ghamdi, A. S. (1995). Time series forecasts for traffic accidents, injuries, and fatalities in Saudi Arabia. Journal of King Saud University-Engineering Sciences, 7(2), 199– 217.
Ariyo, A. A., Adewumi, A. O., & Ayo, C. K. (2014). Stock price prediction using the arima model. In 2014 uksim-amss 16th international conference on computer modelling and simulation (pp. 106–112).
Cameron, A. C., & Trivedi, P. K. (2001). Essentials of count data regression. A companion to theoretical econometrics, 331.
Cameron, A. C., & Trivedi, P. K. (2013). Regression analysis of count data (Vol. 53). Cambridge university press.
Chengye, P., & Ranjitkar, P. (2013). Modelling motorway accidents using negative binomial regression. Journal of the Eastern Asia Society for Transportation Studies, 10, 1946–1963.
Commandeur, J. J., Bijleveld, F. D., Bergel-Hayat, R., Antoniou, C., Yannis, G., & Papadimitriou, E. (2013). On statistical inference in time series analysis of the evolution of road safety. Accident Analysis & Prevention, 60, 424–434.
Elvik, R., Sagberg, F., & Langeland, P. A. (2019). An analysis of factors influencing accidents on road bridges in norway. Accident Analysis & Prevention, 129, 1–6.
Eusofe, Z., & Evdorides, H. (2017). Assessment of road safety management at institutional level in malaysia: A case study. IATSS research, 41(4), 172–181.
Hassouna, F., & Pringle, I. (2019). Analysis and prediction of crash fatalities in australia. The Open Transportation Journal, 13(1).
Ismail, N., & Jemain, A. A. (2007). Handling overdispersion with negative binomial and 45 generalized poisson regression models. In Casualty actuarial society forum (Vol. 2007, pp. 103–58).
Jacobs, G., Aeron-Thomas, A., & Astrop, A. (2000). Estimating global road fatalities.
Jomnonkwao, S., Uttra, S., & Ratanavaraha, V. (2020). Forecasting road traffic deaths in thailand: Applications of time-series, curve estimation, multiple linear regression, and path analysis models. Sustainability, 12(1), 395.
Karlaftis, M. G., & Golias, I. (2002). Effects of road geometry and traffic volumes on rural roadway accident rates. Accident Analysis & Prevention, 34(3), 357–365.
Law, T. H., Umar, R. R., & Wong, S. (2005). The malaysian government’s road accident death reduction target for year 2010. IATSS research, 29(1), 42–49.
Maxwell, O., Mayowa, B. A., Chinedu, I. U., & Peace, A. E. (2018). Modelling count data; a generalized linear model framework. Am J Math Stat, 8(6), 179–183.
Mohd. (2005). Introductory business forecasting : a practical approach. Shah Alam: Pusat Penerbitan Universiti (UPENA).
Mustafa, M. (2006). Management and privatization of road network in bangladesh. In Road engineering association of Asia and Australasia (reaaa) conference, 12th, 2006, philippines.
Naghawi, H. (2018). Negative binomial regression model for road crash severity prediction. Modern Applied Science, 12(4), 38.
Nelder, J. A., & Wedderburn, R. W. (1972). Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135(3), 370–384.
Oppong, R. A. (2012). Statistical analysis of road accidents fatality in ghana using poisson regression (Unpublished doctoral dissertation).
Page, Y. (2001). A statistical model to compare road mortality in oecd countries. Accident Analysis & Prevention, 33(3), 371–385.
Prasetijo, J., & Musa, W. Z. (2016). Modeling zero–inflated regression of road accidents at johor federal road f001. In Matec web of conferences (Vol. 47, p. 03001).
Quddus, M. A. (2008). Time series count data models: an empirical application to traffic accidents. Accident Analysis & Prevention, 40(5), 1732–1741.
Radin, U. (2005). Updates of road safety status in malaysia. IATSS research, 29, 78–80.
Razali, M. S. A., & Zamzuri, Z. (2016). Modeling motorcycle road accidents with traffic offenses at several potential locations using negative binomial regression model in malaysia.
Road safety plan (2021-2030). (n.d.). https://www.mot.gov.my/en/land/safety/ road-safety-plan-2021-2030. (Accessed: 2010-09-30)
Rohayu, S., Sharifah Allyana, S., Jamilah, M., & Wong, S. (2012). Predicting malaysian road fatalities for year 2020.
Sharma, A., & Landge, V. (2013). Zero inflated negative binomial for modeling heavy vehicle crash rate on indian rural highway. International Journal of Advances in Engineering & Technology, 5(2), 292.
Urie, Y., Velaga, N. R., & Maji, A. (2016). Cross-sectional study of road accidents and related law enforcement efficiency for 10 countries: a gap coherence analysis. Traffic injury prevention, 17(7), 686–691.
Wai, A. H. C., Seng, S. Y., & Fei, J. L. W. (2019). Fatality involving road accidents in malaysia: A comparison between three statistical models. In Proceedings of the 2019 2nd international conference on mathematics and statistics (pp. 101–105).
Wittenberg, P., Sever, K., Knoth, S., Sahin, N., & Bondarenko, J. (2013). Determining quantitative road safety targets by applying statistical prediction techniques and a 47 multi-stage adjustment procedure. Accident Analysis & Prevention, 50, 566–577.
Yannis, G., Papadimitriou, E., & Folla, K. (2014). Effect of gdp changes on road traffic fatalities. Safety science, 63, 42–49.
Yu, L. (2012). Using negative binomial regression analysis to predict software faults: A study of apache ant.
Yusria, D. (2017). A time series analysis of road traffic fatalities in malaysia/yusria darma (Unpublished doctoral dissertation). University of Malaya.
Zakaria, R., & Ahmad, Y. (2007). Pemodelan regresi antara kemalangan jalan raya dengan isipadu trafik, persimpangan, lampu isyarat dan waktu kejadian. International Journal of Management Studies, 14(2), 113–125.
In-Text Citation: (Azhari et al., 2022)
To Cite this Article: Azhari, A. H., Zaidi, F. A. M., Anuar, M. H. A., & Othman, J. (2022). A Statistical Analysis of Road Accident Fatalities in Malaysia. International Journal of Academic Research in Business and Social Sciences, 12(5), 1070– 1075.
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