The analysis of road traffic fatality pattern for Selangor, Malaysia case study
Road traffic fatality is a burden towards low- and middle-income countries including Malaysia. Seeing that Selangor has the highest number of road traffic fatalities in Malaysia for the year 2019, therefore the state is selected as a case study. The aim of the article is 1) to understand the road tr...
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Online Access: | http://umpir.ump.edu.my/id/eprint/33673/1/The%20analysis%20of%20road%20traffic%20fatality%20pattern%20for%20Selangor.pdf http://umpir.ump.edu.my/id/eprint/33673/ https://doi.org/10.15282/mekatronika.v3i1.7155 https://doi.org/10.15282/mekatronika.v3i1.7155 |
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my.ump.umpir.336732022-04-11T03:14:04Z http://umpir.ump.edu.my/id/eprint/33673/ The analysis of road traffic fatality pattern for Selangor, Malaysia case study Radzuan, N. Q. Mohd Hasnun Ariff, Hassan Abu Kassim, K. A. Ab. Rashid, A. A. Intan Suhana, Mohd Razelan Nur Aqilah, Othman HE Transportation and Communications TE Highway engineering. Roads and pavements TK Electrical engineering. Electronics Nuclear engineering Road traffic fatality is a burden towards low- and middle-income countries including Malaysia. Seeing that Selangor has the highest number of road traffic fatalities in Malaysia for the year 2019, therefore the state is selected as a case study. The aim of the article is 1) to understand the road traffic crash pattern and road traffic fatality pattern in Selangor 2) to determine the ability of 16 road traffic features in classifying road traffic fatality occurrence. The preliminary data screening shows that road traffic crash patterns and road traffic fatality patterns in Selangor have many similarities. However, both of them also have few dissimilarities such as crash time of occurrence, day of occurrence, number of vehicles involved in a crash, and type of vehicle first hit for the crash. Supervised machine learning algorithm in Orange data mining software was considered in this analysis. The analysed algorithms among others are neural network, random forest, decision tree, logistic regression, naïve Bayes, and support vector machine. Neural network was seen as the best algorithm to classify road traffic fatality occurrence with 97.0% classification accuracy outperform other algorithms. The result of the article can be used by the relevant traffic stakeholders to execute safety intervention in a more focused manner in Selangor to reduce the number of road traffic fatalities. Penerbit UMP 2021 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/33673/1/The%20analysis%20of%20road%20traffic%20fatality%20pattern%20for%20Selangor.pdf Radzuan, N. Q. and Mohd Hasnun Ariff, Hassan and Abu Kassim, K. A. and Ab. Rashid, A. A. and Intan Suhana, Mohd Razelan and Nur Aqilah, Othman (2021) The analysis of road traffic fatality pattern for Selangor, Malaysia case study. Mekatronika - Journal of Intelligent Manufacturing & Mechatronics, 3 (1). pp. 79-88. ISSN 2637-0883 https://doi.org/10.15282/mekatronika.v3i1.7155 https://doi.org/10.15282/mekatronika.v3i1.7155 |
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HE Transportation and Communications TE Highway engineering. Roads and pavements TK Electrical engineering. Electronics Nuclear engineering Radzuan, N. Q. Mohd Hasnun Ariff, Hassan Abu Kassim, K. A. Ab. Rashid, A. A. Intan Suhana, Mohd Razelan Nur Aqilah, Othman The analysis of road traffic fatality pattern for Selangor, Malaysia case study |
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Road traffic fatality is a burden towards low- and middle-income countries including Malaysia. Seeing that Selangor has the highest number of road traffic fatalities in Malaysia for the year 2019, therefore the state is selected as a case study. The aim of the article is 1) to understand the road traffic crash pattern and road traffic fatality pattern in Selangor 2) to determine the ability of 16 road traffic features in classifying road traffic fatality occurrence. The preliminary data screening shows that road traffic crash patterns and road traffic fatality patterns in Selangor have many similarities. However, both of them also have few dissimilarities such as crash time of occurrence, day of occurrence, number of vehicles involved in a crash, and type of vehicle first hit for the crash. Supervised machine learning algorithm in Orange data mining software was considered in this analysis. The analysed algorithms among others are neural network, random forest, decision tree, logistic regression, naïve Bayes, and support vector machine. Neural network was seen as the best algorithm to classify road traffic fatality occurrence with 97.0% classification accuracy outperform other algorithms. The result of the article can be used by the relevant traffic stakeholders to execute safety intervention in a more focused manner in Selangor to reduce the number of road traffic fatalities. |
format |
Article |
author |
Radzuan, N. Q. Mohd Hasnun Ariff, Hassan Abu Kassim, K. A. Ab. Rashid, A. A. Intan Suhana, Mohd Razelan Nur Aqilah, Othman |
author_facet |
Radzuan, N. Q. Mohd Hasnun Ariff, Hassan Abu Kassim, K. A. Ab. Rashid, A. A. Intan Suhana, Mohd Razelan Nur Aqilah, Othman |
author_sort |
Radzuan, N. Q. |
title |
The analysis of road traffic fatality pattern for Selangor, Malaysia case study |
title_short |
The analysis of road traffic fatality pattern for Selangor, Malaysia case study |
title_full |
The analysis of road traffic fatality pattern for Selangor, Malaysia case study |
title_fullStr |
The analysis of road traffic fatality pattern for Selangor, Malaysia case study |
title_full_unstemmed |
The analysis of road traffic fatality pattern for Selangor, Malaysia case study |
title_sort |
analysis of road traffic fatality pattern for selangor, malaysia case study |
publisher |
Penerbit UMP |
publishDate |
2021 |
url |
http://umpir.ump.edu.my/id/eprint/33673/1/The%20analysis%20of%20road%20traffic%20fatality%20pattern%20for%20Selangor.pdf http://umpir.ump.edu.my/id/eprint/33673/ https://doi.org/10.15282/mekatronika.v3i1.7155 https://doi.org/10.15282/mekatronika.v3i1.7155 |
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1731225789430497280 |
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13.18916 |