(Faculty of Computer Science & Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia)(2) Cik Feresa Mohd Foozy
(Faculty of Computer Science & Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia)(3) Aida Mustapha
(Faculty of Computer Science & Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia)(4) * Salama A Mostafa
(Faculty of Computer Science & Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia)(5) Shamala Palaniappan
(Faculty Science Computer and Mathematics, Universiti Teknologi MARA (UiTM), Malaysia)(6) Shafiza Ariffin Kashinath
(Sena Traffic Systems Sdn. Bhd, Kuala Lumpur, Malaysia)*corresponding author
AbstractTraffic summons, also known as traffic tickets, is a notice issued by a law enforcement official to a motorist, who is a person who drives a car, lorry, or bus, and a person who rides a motorcycle. This study is set to perform a comparative experiment to compare the performance of three classification algorithms (Naive Bayes, Gradient Boosted Trees, and Deep Learning algorithm) in classifying the traffic violation types. The performance of all the three classification models developed in this work is measured and compared. The results show that the Gradient Boosted Trees and Deep Learning algorithm have the best value in accuracy and recall but low precision. Naïve Bayes, on the other hand, has high recall since it is a picky classifier that only performs well in a dataset that is high in precision. This paper’s results could serve as baseline results for investigations related to the classification of traffic violation types. It is also helpful for authorities to strategize and plan ways to reduce traffic violations among road users by studying the most common traffic violation types in an area, whether a citation, a warning, or an ESERO (Electronic Safety Equipment Repair Order).
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DOIhttps://doi.org/10.26555/ijain.v7i3.708 |
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