Abstract
Malaysia has recorded a steady increase in the number of road traffic accidents from year to year at an alarming rate of 5%. Serious injuries due to the accidents, which could lead to permanent disability, might cause a long-term problem to the nation economy-wise. Predicting the number of serious injury cases in the future is important in understanding the trend of road traffic accidents to help policymakers in proposing a countermeasure. Time-series model has been employed to predict the occurrence of road traffic crashes including fatalities. Nonetheless, the prediction of serious injury cases, which should not be taken lightly due to its potential impact, has not been proposed especially with regards to Malaysian road traffic accident data. This study attempts to employ artificial neural networks (ANN), a machine learning algorithm, to predict the number of serious injury cases in Malaysia based on the road traffic accident data of the past 20 years. Machine learning has increasingly been adopted in recent years owing to its ability to predict as well as catering for the non-linear behaviour of the data examined. A single-hidden ANN model was developed based on seven features, namely the number of registered vehicles, population, length of federal road, length of FELDA road, length of federal institutional road, length of federal territory road, and length of the expressway in order to predict the number of serious injuries. It was established from the present investigation that the developed ANN model is capable to predict the number of serious injuries from 1997 until 2017 with a mean absolute percentage error of only 3%. This demonstrates the capability of the developed machine learning in road traffic accident prediction, and it could be useful in outlining an action plan to mitigate the number of serious injuries in Malaysia.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jabatan Keselamatan Jalan Raya Malaysia: Buku Statistik Keselamatan Jalan Raya (2018)
Jabatan Kerja Raya: Statistik Jalan Edisi 2016. Jabatan Kerja Raya, Kuala Lumpur (2016)
Olsson, M., Järbrink, K., Divakar, U., Bajpai, R., Upton, Z., Schmidtchen, A., Car, J.: The humanistic and economic burden of chronic wounds: a systematic review. Wound Rep. Reg. 27(1), 114–125 (2019)
Mcleod, A.I., Vingilis, E.R.: Power computations in time series analysis for traffic safety interventions. Accid. Anal. Prev. 40(3), 1244–1248 (2008)
Radin Umar, R.S.: Model kematian jalan raya di Malaysia: unjuran tahun 2000. Pertanika J. Sci. Technol. 6(2), 107–119 (1998)
Sarani, R., Syed Mohamed Rahim, S.A., Mohd Marjan, J., Wong, S.V.: Predicting Malaysian Road Fatalities for year 2020, MRR 06/2012, Malaysian Institute of Road Safety Research, Kuala Lumpur (2012)
Musa, R.M., Majeed, A.P.P.A., Taha, Z., Siow, W.C., Ab Nasir, A.F., Abdullah, M.R.: A machine learning approach of predicting high potential archers by means of physical fitness indicators. PLoS ONE 14(1), e0209638 (2019)
Taha, Z., Musa, R.M., Abdul Majeed, A.P.P., Abdullah, M.R., Ab Nasir, A.F., Hassan, M.H.A.: Classification of high performance archers by means of bio-physiological performance variables via k-nearest neighbour classification model. In: Hassan, M. (ed.) Intelligent Manufacturing and Mechatronics. Lecture Notes in Mechanical Engineering. Springer, Singapore (2018)
Yusri, I.M., Majeed, A.A., Mamat, R., Ghazali, M.F., Awad, O.I., Azmi, W.H.: A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel. Renew. Sustain. Energ. Rev. 90, 665–686 (2018)
Acknowledgements
The authors would like to acknowledge ASEAN NCAP, FIA Foundation, Global NCAP, OEMs, Malaysian Institute of Road Safety Research (MIROS) and the Society of Automotive Engineers Malaysia (SAE Malaysia) for funding this study under the ASEAN NCAP Holistic Collaborative Research (ANCHOR II) grant. Also, the authors are thankful to the Universiti Malaysia Pahang for providing the facilities to conduct the study.
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
Radzuan, N.Q., Hassan, M.H.A., Abdul Majeed, A.P.P., Musa, R.M., Mohd Razman, M.A., Abu Kassim, K.A. (2020). Predicting Serious Injuries Due to Road Traffic Accidents in Malaysia by Means of Artificial Neural Network. In: Jamaludin, Z., Ali Mokhtar, M.N. (eds) Intelligent Manufacturing and Mechatronics. SympoSIMM 2019. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-13-9539-0_8
Download citation
DOI: https://doi.org/10.1007/978-981-13-9539-0_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9538-3
Online ISBN: 978-981-13-9539-0
eBook Packages: EngineeringEngineering (R0)