Forecasting road deaths in Malaysia using support vector machine

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...

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Main Authors:	Nurul Qastalani, Radzuan, Mohd Hasnun, Arif Hassan, Anwar P.P., Abdul Majeed, Rabiu Muazu, Musa, Khairil Anwar, Abu Kassim
Format:	Article
Language:	English
Published:	Springer 2020
Subjects:	TD Environmental technology. Sanitary engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/27603/ http://umpir.ump.edu.my/id/eprint/27603/1/Road%20Death%20SVM_submitted_citation.docm

id	ump-27603
recordtype	eprints
spelling	ump-276032020-01-31T09:03:16Z http://umpir.ump.edu.my/id/eprint/27603/ Forecasting road deaths in Malaysia using support vector machine Nurul Qastalani, Radzuan Mohd Hasnun, Arif Hassan Anwar P.P., Abdul Majeed Rabiu Muazu, Musa Khairil Anwar, Abu Kassim TD Environmental technology. Sanitary engineering 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 counter measure to bring down the death toll. A predictive model of Malaysian road death has been developed using a time-series model known as auto regressive 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 poli-cies and regulations to reduce road fatalities in Malaysia. Springer 2020 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/27603/1/Road%20Death%20SVM_submitted_citation.docm Nurul Qastalani, Radzuan and Mohd Hasnun, Arif Hassan and Anwar P.P., Abdul Majeed and Rabiu Muazu, Musa and Khairil Anwar, Abu Kassim (2020) Forecasting road deaths in Malaysia using support vector machine. The 5th International Conference on Electrical, Control & Computer Engineering (InECCE 2019). ISSN ISBN:978-981-15-2317-5 (In Press)
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TD Environmental technology. Sanitary engineering
spellingShingle	TD Environmental technology. Sanitary engineering Nurul Qastalani, Radzuan Mohd Hasnun, Arif Hassan Anwar P.P., Abdul Majeed Rabiu Muazu, Musa Khairil Anwar, Abu Kassim Forecasting road deaths in Malaysia using support vector machine
description	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 counter measure to bring down the death toll. A predictive model of Malaysian road death has been developed using a time-series model known as auto regressive 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 poli-cies and regulations to reduce road fatalities in Malaysia.
format	Article
author	Nurul Qastalani, Radzuan Mohd Hasnun, Arif Hassan Anwar P.P., Abdul Majeed Rabiu Muazu, Musa Khairil Anwar, Abu Kassim
author_facet	Nurul Qastalani, Radzuan Mohd Hasnun, Arif Hassan Anwar P.P., Abdul Majeed Rabiu Muazu, Musa Khairil Anwar, Abu Kassim
author_sort	Nurul Qastalani, Radzuan
title	Forecasting road deaths in Malaysia using support vector machine
title_short	Forecasting road deaths in Malaysia using support vector machine
title_full	Forecasting road deaths in Malaysia using support vector machine
title_fullStr	Forecasting road deaths in Malaysia using support vector machine
title_full_unstemmed	Forecasting road deaths in Malaysia using support vector machine
title_sort	forecasting road deaths in malaysia using support vector machine
publisher	Springer
publishDate	2020
url	http://umpir.ump.edu.my/id/eprint/27603/ http://umpir.ump.edu.my/id/eprint/27603/1/Road%20Death%20SVM_submitted_citation.docm
first_indexed	2023-09-18T22:43:22Z
last_indexed	2023-09-18T22:43:22Z
_version_	1777417063312130048

Forecasting road deaths in Malaysia using support vector machine

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