车联网环境下基于模糊逻辑的交通拥堵检测方法.

Because of the drawback of existing method, the paper proposed a traffic congestion detection method based on Internet of Vehicles and fuzzy logic. Firstly, this method constructed a neighbor table of vehicle based on the obtained real-time information of neighbor vehicles. Secondly, it created a fuzzy controller according to the speed, traffic density and traffic congestion rating system, and evaluated the local traffic congestion level. Thirdly, it realized the inquiry of traffic congestion status of neighbor vehicles based on real-time communication between neighbor vehicles, and verified the local traffic congestion level based on large sub-sample hypothesis test, then completed the detection of regional traffic congestion level. Finally, it built a simulated test scenario based on Veins platform, then it compared the accuracy of detecting traffic congestion, and calculated the backoff slots and received broadcast packets of vehicle nodes in the simulated test scenario. The results show that the accuracy of traffic congestion detection of the proposed method is increased by 5. 5% and 7 . 5% compared with " no trigger" method and CoTEC method, respectively. The proposed method reduces the network congestion of vehicle communication by 90. 8% compared with CoTEC method, and the communication load of communication nodes significantly decreases when there is no traffic congestion. [ABSTRACT FROM AUTHOR]