Forecasting Road Deaths in Malaysia Using Support Vector Machine

Nurul Qastalani Radzuan, Mohd Hasnun Arif Hassan*, Anwar P.P.Abdul Majeed, Khairil Anwar Abu Kassim, Rabiu Muazu Musa, Mohd Azraai Mohd Razman, Nur Aqilah Othman

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

4 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationInECCE 2019 - Proceedings of the 5th International Conference on Electrical, Control and Computer Engineering
EditorsAhmad Nor Kasruddin Nasir, Mohd Mawardi Saari, Mohd Razali Daud, Ahmad Afif Mohd Faudzi, Mohd Ashraf Ahmad, Muhammad Sharfi Najib, Yasmin Abdul Wahab, Nur Aqilah Othman, Nor Maniha Abd Ghani, Addie Irawan, Sabira Khatun, Raja Mohd Taufika Raja Ismail
PublisherSpringer
Pages261-267
Number of pages7
ISBN (Print)9789811523168
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event5th International Conference on Electrical, Control and Computer Engineering, InECCE 2019 - Kuantan, Malaysia
Duration: 29 Jul 201929 Jul 2019

Publication series

NameLecture Notes in Electrical Engineering
Volume632
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference5th International Conference on Electrical, Control and Computer Engineering, InECCE 2019
Country/TerritoryMalaysia
CityKuantan
Period29/07/1929/07/19

Keywords

  • Machine learning
  • Prediction
  • Road death
  • Road traffic accident
  • Support vector machine

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