V2V‐based method for the detection of road traffic congestion

Abstract

The traffic congestion detection based on the internet of vehicles is gaining enormous research interest. A vehicle-to-vehicle (V2V)-based method for the detection of road traffic congestion is proposed. Firstly, a fuzzy controller was constructed based on the vehicle speed, traffic density, and traffic congestion rating system, and the level of local traffic congestion was evaluated. Then, the level of local traffic congestion of neighbouring vehicles was queried based on V2V communication, and the level of regional traffic congestion was obtained based on a large sub-sample hypothesis test. Finally, a simulation test platform was built based on vehicles in network simulation, and the back-off time slots and received packets of vehicle nodes were calculated. The accuracy of the proposed method for detecting road traffic congestion was compared to the cooperative traffic congestion detection (CoTEC) method and the geomagnetic coil method. The results show that the detection accuracy of the proposed method increased by 5.5 and 7.5%, respectively, compared to the geomagnetic coil method and CoTEC method. The V2V communication network overhead of the proposed traffic congestion detection method is reduced by 90.8% compared to the adopted CoTEC method. The communication overhead of the vehicle node using the proposed method is significantly decreased when there is no traffic congestion.