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

doi:10.1007/978-981-15-2317-5_22

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

Universiti Malaysia Pahang Al-Sultan Abdullah
Faculty of Manufacturing and Mechatronics Engineering Technology
Malaysian Institute of Road Safety Research (MIROS)
Universiti Malaysia Terengganu
Faculty of Electrical and Electronics Engineering

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

5 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 language	English
Title of host publication	InECCE 2019 - Proceedings of the 5th International Conference on Electrical, Control and Computer Engineering
Editors	Ahmad 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
Publisher	Springer
Pages	261-267
Number of pages	7
ISBN (Print)	9789811523168
DOIs	https://doi.org/10.1007/978-981-15-2317-5_22
Publication status	Published - 2020
Externally published	Yes
Event	5th International Conference on Electrical, Control and Computer Engineering, InECCE 2019 - Kuantan, Malaysia Duration: 29 Jul 2019 → 29 Jul 2019

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	632
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	5th International Conference on Electrical, Control and Computer Engineering, InECCE 2019
Country/Territory	Malaysia
City	Kuantan
Period	29/07/19 → 29/07/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Machine learning
Prediction
Road death
Road traffic accident
Support vector machine

Access to Document

10.1007/978-981-15-2317-5_22

Cite this

Radzuan, N. Q., Hassan, M. H. A., Majeed, A. P. P. A., Kassim, K. A. A., Musa, R. M., Razman, M. A. M., & Othman, N. A. (2020). Forecasting Road Deaths in Malaysia Using Support Vector Machine. In A. N. Kasruddin Nasir, M. M. Saari, M. R. Daud, A. A. Mohd Faudzi, M. A. Ahmad, M. S. Najib, Y. Abdul Wahab, N. A. Othman, N. M. Abd Ghani, A. Irawan, S. Khatun, & R. M. T. Raja Ismail (Eds.), InECCE 2019 - Proceedings of the 5th International Conference on Electrical, Control and Computer Engineering (pp. 261-267). (Lecture Notes in Electrical Engineering; Vol. 632). Springer. https://doi.org/10.1007/978-981-15-2317-5_22

Radzuan, Nurul Qastalani ; Hassan, Mohd Hasnun Arif ; Majeed, Anwar P.P.Abdul et al. / Forecasting Road Deaths in Malaysia Using Support Vector Machine. InECCE 2019 - Proceedings of the 5th International Conference on Electrical, Control and Computer Engineering. editor / Ahmad 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. Springer, 2020. pp. 261-267 (Lecture Notes in Electrical Engineering).

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title = "Forecasting Road Deaths in Malaysia Using Support Vector Machine",

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.",

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note = "Funding Information: 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. Funding Information: 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. Publisher Copyright: {\textcopyright} 2020, Springer Nature Singapore Pte Ltd.; 5th International Conference on Electrical, Control and Computer Engineering, InECCE 2019 ; Conference date: 29-07-2019 Through 29-07-2019",

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language = "English",

isbn = "9789811523168",

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pages = "261--267",

editor = "\{Kasruddin Nasir\}, \{Ahmad Nor\} and Saari, \{Mohd Mawardi\} and Daud, \{Mohd Razali\} and \{Mohd Faudzi\}, \{Ahmad Afif\} and Ahmad, \{Mohd Ashraf\} and Najib, \{Muhammad Sharfi\} and \{Abdul Wahab\}, Yasmin and Othman, \{Nur Aqilah\} and \{Abd Ghani\}, \{Nor Maniha\} and Addie Irawan and Sabira Khatun and \{Raja Ismail\}, \{Raja Mohd Taufika\}",

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Radzuan, NQ, Hassan, MHA, Majeed, APPA, Kassim, KAA, Musa, RM, Razman, MAM & Othman, NA 2020, Forecasting Road Deaths in Malaysia Using Support Vector Machine. in AN Kasruddin Nasir, MM Saari, MR Daud, AA Mohd Faudzi, MA Ahmad, MS Najib, Y Abdul Wahab, NA Othman, NM Abd Ghani, A Irawan, S Khatun & RMT Raja Ismail (eds), InECCE 2019 - Proceedings of the 5th International Conference on Electrical, Control and Computer Engineering. Lecture Notes in Electrical Engineering, vol. 632, Springer, pp. 261-267, 5th International Conference on Electrical, Control and Computer Engineering, InECCE 2019, Kuantan, Malaysia, 29/07/19. https://doi.org/10.1007/978-981-15-2317-5_22

Forecasting Road Deaths in Malaysia Using Support Vector Machine. / Radzuan, Nurul Qastalani; Hassan, Mohd Hasnun Arif; Majeed, Anwar P.P.Abdul et al.
InECCE 2019 - Proceedings of the 5th International Conference on Electrical, Control and Computer Engineering. ed. / Ahmad 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. Springer, 2020. p. 261-267 (Lecture Notes in Electrical Engineering; Vol. 632).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

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AU - Othman, Nur Aqilah

N1 - Funding Information: 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. Funding Information: 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. Publisher Copyright: © 2020, Springer Nature Singapore Pte Ltd.

PY - 2020

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N2 - 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.

AB - 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.

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DO - 10.1007/978-981-15-2317-5_22

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Radzuan NQ, Hassan MHA, Majeed APPA, Kassim KAA, Musa RM, Razman MAM et al. Forecasting Road Deaths in Malaysia Using Support Vector Machine. In Kasruddin Nasir AN, Saari MM, Daud MR, Mohd Faudzi AA, Ahmad MA, Najib MS, Abdul Wahab Y, Othman NA, Abd Ghani NM, Irawan A, Khatun S, Raja Ismail RMT, editors, InECCE 2019 - Proceedings of the 5th International Conference on Electrical, Control and Computer Engineering. Springer. 2020. p. 261-267. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-15-2317-5_22

Forecasting Road Deaths in Malaysia Using Support Vector Machine

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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