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InECCE2019 pp 261-267 | Cite as

Forecasting Road Deaths in Malaysia Using Support Vector Machine

  • Nurul Qastalani Radzuan
  • Mohd Hasnun Arif HassanEmail author
  • Anwar P. P. Abdul Majeed
  • Khairil Anwar Abu Kassim
  • Rabiu Muazu Musa
  • Mohd Azraai Mohd Razman
  • Nur Aqilah Othman
Conference paper
  • 26 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 632)

Abstract

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.

Keywords

Road traffic accident Road death Prediction Machine learning Support vector machine 

Notes

Acknowledgements

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.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Nurul Qastalani Radzuan
    • 1
  • Mohd Hasnun Arif Hassan
    • 1
    Email author
  • Anwar P. P. Abdul Majeed
    • 2
  • Khairil Anwar Abu Kassim
    • 4
  • Rabiu Muazu Musa
    • 5
  • Mohd Azraai Mohd Razman
    • 2
  • Nur Aqilah Othman
    • 3
  1. 1.Faculty of Mechanical and Automotive Engineering TechnologyUniversiti Malaysia PahangPekanMalaysia
  2. 2.Faculty of Manufacturing and Mechatronics Engineering TechnologyUniversiti Malaysia PahangPekanMalaysia
  3. 3.Faculty of Electrical and Electronics Engineering TechnologyPekanMalaysia
  4. 4.Malaysian Institute of Road Safety Research (MIROS)KajangMalaysia
  5. 5.Centre for Fundamental and Liberal EducationUniversiti Malaysia TerengganuKuala NerusMalaysia

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