On the Development of a Deep Learning Model for Profiling and Predicting Traffic Offenders: OOADM Approach

Abstract

Monitoring of traffic offenders in developing countries has a lot of challenges including; lack of proper authentication of vehicles and users, lack of substantive traffic system that suits the management of traffic offenders’ profile in both rural and urban areas, lack of predictable modules to forecast the tendency of an offender to cause accident in the future, poor means of communication between traffic agencies and vehicle users, poor traffic offence awareness for vehicle users and lack of a dependable traffic offenders profile database. Therefore, this thesis is providing a solution by development of a deep learning model for profiling and predicting traffic offenders focuses on developing a traffic offenders profiling and prediction system using deep learning algorithm to predict the likelihood of an offence to be committed by a road user. The proposed system developed a model that will profile traffic offenders in both urban and rural settings, create a traffic offender’s database that will interact with existing national databases to authenticate traffic offenders, provides a module that will predict the likelihood of a road user to commit severe traffic blunder in the future and provide intelligent information necessary for timely action by law enforcement agencies. The system designs was implemented using a web-system developed with PHP, MySQL and JavaScript. The System Design followed the OODM methodology for componentization of the system modules giving room for coupling, decoupling, modification, encapsulation and reuse, as well as easy maintainability. Unified Modeling Language was extensively used to simplify the explanation of the system modules. The software performance was tested using accuracy of traffic offender prediction and Confusion Matrix was

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