A Survey of Road Traffic Management Vehicular Network Systems PROJECT TITLE : A Survey of Vehicular Network Systems for Road Traffic Management ABSTRACT: Within the scope of this survey, we investigate and evaluate various proposals for vehicular Communication systems in relation to the administration of road traffic. Beginning with the definition of Communications between vehicles (V2V), vehicles to infrastructure (V2I), and vehicles to everything else (V2X), our initial focus will be on the requirements and current standards for Intelligent Transport Systems (ITS). These requirements and standards will include the maximum Communication delay, the Communication range, and the size of messages (in the case of V2I transmission). Following this, we conduct an analysis of the use cases associated with the implementation of intelligent traffic management and conduct a review of the respective methods that either indirectly or directly manage traffic on roads. One of the primary goals of this paper is to highlight the architectures of four classes of systems that are able to support vehicular traffic management and Communication between vehicles and roadside infrastructure. These four classes of systems are vehicular Cloud Computing (VCC), cloudlets, Mobile Edge Computing (MEC), and fog computing. One of the primary objectives of this paper is to highlight the architectures of four classes of systems that are able to support vehicular traffic management. In this context, we also present our categorization of the methods that correspond to these four categories of architectural layouts. In conclusion, we offer our assessment of the challenges and constraints associated with the deployment of mechanisms that are associated with each of the architecture classes that were taken into consideration. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest A VANET Routing Scheme Based on Intelligent Machine Learning In VANET, a novel Hypergraph Clustering Model (HGCM) for Urban Scenarios is presented.