Real-time perception of urban road congestion based on internet of vehicles technology

In order to solve the problems of high packet loss rate, low perception accuracy and long perception time existing in traditional urban road congestion real-time sensing methods, this paper proposes a new urban road congestion real-time sensing method based on Internet of Vehicles technology. The Internet of Vehicles technology is used to collect urban road data, and the collected data is cleaned and repaired. On this basis, the k-Nearest Neighbor algorithm is used to estimate the travel time of vehicles on the current road, and the grade of urban road congestion state is designed. Combined with the travel time estimation results, the real-time perception of urban road congestion state is realized. The experimental results show that the packet loss rate of this method is always below 5%, the perceptual accuracy is above 96%, and the average time is 0.61s, the practical application effect is good. © 2021, Aracne Editrice. All rights reserved.