Open AccessA data mining approach for classification of traffic violations types
Author(s) -
Nor Aqilah Othman,
Cik Feresa Mohd Foozy,
Aida Mustapha,
Salama A. Mostafa,
Shamala Palaniappan,
Shafiza Ariffin Kashinath
Publication year - 2021
Publication title -
ijain (international journal of advances in intelligent informatics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.183
H-Index - 9
eISSN - 2548-3161
pISSN - 2442-6571
DOI - 10.26555/ijain.v7i3.708
Subject(s) - computer science , naive bayes classifier , precision and recall , notice , artificial intelligence , law enforcement , machine learning , traffic classification , classifier (uml) , data mining , computer security , support vector machine , network packet , political science , law
Traffic 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).