Published Paper Details:

This project presents the prediction of traffic-violations using machine learning, more specifically, when most likely a traffic- violation may happen. Also, what are the contributing factors that may cause more damages (e.g., personal injury, property damage, etc.) are discussed in this work. The national database for trafficviolation was considered for the mining and analyzed results indicated that a few specific times are probable for traffic-violations. Moreover, most accidents happened on specific days and times. The findings of this work could help prevent some trafficviolations or reduce the chance of occurrence. These results can be used to increase cautions and traffic-safety tips. This work 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 random forest which was executed using an open source application named PyCharm. Finally, the developed application was evaluated by Python.