Road and Traffic Violation Data Analytics Using Random Forest
Cindy AgnoBalabat Aurora1, Maria Visitacion Gumabay2

1Cindy AgnoBalabat Aurora, Liceo De Cagayan University, Cagayan De Oro City Philippines.
2Maria Visitacion Gumabay, Saint Paul University, Philippines, Cagayan Philippines
Manuscript received on 07 March 2019 | Revised Manuscript received on 20 March 2019 | Manuscript published on 30 March 2019 | PP: 1121-1127 | Volume-8 Issue-5, March 2019 | Retrieval Number: E3365038519/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: This paper presents an in-depth analysis of road and traffic violations pattern using Data Analytics methods, aimed at improving road and traffic management, government planning and decision making. The study identified the road and traffic current management practice as basis of the design development and implementation of the road and traffic management system. The application managed all the road and traffic violation that will produce recorded set for analysis, which carried out from over of five years. Through data cleansing a total of twenty thousand six hundred forty record set was derived. It is important to find use of this record set, build analysis models, and use interactive tools to produce predictive data, understand the relevance, trends, and driving behaviors from the road and traffic violations data in terms of the following predictors: gender of the violator, vehicle owner address, location of violation, month and time the violation was committed and traffic enforcer who issued the citation. The study was able to establish a data analysis model by using a powerful classification and regression tool – random forest which was executed using an open source application named Orange. Finally, the developed application was evaluated by system users and IT experts using the ISO 25010 criteria.
Keyword: Road And Traffic, Violation, Data Analytics, Random Forest.
Scope of the Article: Data Analytics