The analysis of road traffic fatality pattern for Selangor, Malaysia case study

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. (Published)

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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.

Item Type: Article
Uncontrolled Keywords: Road traffic crash pattern; Road traffic fatality pattern; Artificial neural network; Supervised machine learning
Subjects: H Social Sciences > HE Transportation and Communications
T Technology > TE Highway engineering. Roads and pavements
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Institute of Postgraduate Studies
Faculty of Civil Engineering Technology
Faculty of Electrical and Electronic Engineering Technology
Faculty of Mechanical and Automotive Engineering Technology
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 11 Apr 2022 03:14
Last Modified: 11 Apr 2022 03:14
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