Applying Social Internet of Vehicles in Smart Cities to Reduce Traffic Congestion

Abstract

Traffic congestion issues are getting worse today as the number of vehicles on the road is increasing exponentially at an unprecedented rate. These roads have limited space but the number of huge vehicles on the roads. As a result, in many cities around the world, traffic congestion is a serious problem. Vehicle congestion is a severe issue since it has an impact on several aspects of travel, including time spent traveling, distance to the destination, fuel consumption, and environmental pollution. The Internet of Vehicles (IoV) has been able to include social components to build a new intelligent transportation system termed the Social Internet of Vehicles (SIoV) as a result of technological advancements and social transformation. The evaluation of real-time traffic data patterns is the most unpleasant challenge in smart cities, and based on that real-time data, it should be simple to estimate the prospective amount of traffic congestion. In addition, it can be challenging to forecast the likelihood of congestion on a given road. Several technologies are existing to resolve congestion-free traffic. By using SIoV we overcome the traffic congestion in smart cities. We used the Simulation of Urban Mobility (SUMO) tool for vehicle-to-vehicle communication to reduce traffic congestion. We evaluate our proposed scheme with existing work and also on the basis of message size, upload, and response time.