Forecasting Road Deaths in Malaysia Using Support Vector Machine
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An average of 6,350 people died every year in Malaysia due to road traffic accidents. A published data of Malaysian road deaths in 20 years since 1997 reveals that the number of fatalities has not really declined with a difference of less than 10% from one year to the next. Forecasting the number of fatalities is beneficial in planning a countermeasure to bring down the death toll. A predictive model of Malaysian road death has been developed using a time-series model known as autoregressive integrated moving average (ARIMA). The model was used in the previous Road Safety Plan of Malaysia to set a target death toll to be reduced in 2020, albeit being inaccurate. This study proposes a new approach in forecasting the road deaths, by means of a machine learning algorithm known as Support Vector Machine. The length of various types of road, number of registered vehicles and population were among the eight features used to develop the model. Comparison between the actual road deaths and the prediction demonstrates a good agreement, with a mean absolute percentage error of 2% and an R-squared value of 85%. The Linear kernel-based Support Vector Machine was found to be able to predict the road deaths in Malaysia with reasonable accuracy. The developed model could be used by relevant stakeholders in devising appropriate policies and regulations to reduce road fatalities in Malaysia.
KeywordsRoad traffic accident Road death Prediction Machine learning Support vector machine
The authors would like to acknowledge ASEAN NCAP, FIA Foundation, Global NCAP, OEMs and the Society of Automotive Engineers Malaysia (SAE Malaysia) for funding this study under the ASEAN NCAP Holistic Collaborative Research (ANCHOR II) grant (UIC191504). Also, the authors are thankful to the Universiti Malaysia Pahang for providing the facilities to conduct the study.
- 1.Department of Statistics Malaysia: Statistics on Causes of Death, Malaysia (2018)Google Scholar
- 2.Radin Umar RS (1998) Model kematian jalan raya di Malaysia: unjuran tahun 2000. Pertanika J Sci Technol 6(2)Google Scholar
- 3.Sarani R, Syed Mohamed Rahim SA, Mohd Marjan J, Wong SV (2012) Predicting Malaysian road fatalities for year 2020, MRR 06/2012. Malaysian Institute of Road Safety Research, Kuala LumpurGoogle Scholar
- 4.Ministry of Transport Malaysia: Road Safety Plan of Malaysia 2014–2020 (2014)Google Scholar
- 5.World Highways (2017) Malaysia’s road safety problem needs addressing. Route One Publishing Ltd., Kent, United KingdomGoogle Scholar
- 6.Darma Y (2017) A time series analysis of road traffic fatalities in Malaysia. University of MalayaGoogle Scholar
- 7.Road Safety Department Malaysia (2018) Buku Statistik Keselamatan Jalan Raya. Kuala Lumpur, MalaysiaGoogle Scholar
- 8.Hartika HA, Ramli MZ, Zaihafiz M, Abidin Z, Hafiz M (2017) Study of road accident prediction model at accident blackspot area: a case study at Selangor 3(5): 466–470Google Scholar
- 9.Taha Z, Razman MAM, Adnan FA, Abdul Ghani AS, Abdul Majeed APP, Musa RM, Sallehudin MF, Mukai Y (2018) The identification of hunger behaviour of Lates calcarifer through the integration of image processing technique and support vector machine. In: IOP conference series: materials science and engineering, vol 319, no 1Google Scholar
- 10.Taha Z, Musa RM, Majeed APPA, Abdullah MR, Abdullah MA, Hassan MHA, Khalil Z (2018) The employment of support vector machine to classify high and low performance archers based on bio-physiological variables. In: IOP conference series: materials science and engineering, vol 342, no 1Google Scholar
- 11.Public Works Department Malaysia (2016) Statistik Jalan Edisi 2016. Kuala Lumpur, MalaysiaGoogle Scholar