Speed Guidance Model in the Vicinity of Intersections Based on Internet of Vehicles

In the context of the rapid development of urbanization and motorization, road congestion, environmental pollution caused by traffic, and traffic accidents have become a “city disease” that attracts much attention. To solve these problems, the development of vehicle-to-vehicle, automatic driving technology, and other science and technology is constantly giving new connotation to traffic flow. The application of these technologies makes urban roads gradually transform from the traditional traffic flow composed of Human Driven Vehicles (HDVs) to the traffic flow composed of Intelligent and Connected Vehicles (ICVs). Domestic research on speed guidance is relatively late, and most of them are the influence of speed guidance on expressways, but there are few signal-controlled intersections. Therefore, this paper proposes a speed guidance model based on the Cooperative Adaptive Cruise Control (CACC) model calibrated by PATH Laboratory of the University of California, Berkeley through real car experiments, and compares it with the driving model based on Intelligent Driver Model (IDM) as the driving model of artificial driving vehicles. Aiming at the signalized intersection area of the urban road, the acceleration, speed and position change of vehicles are studied by simulation experiment. The results show that in the 100s simulation experiment, ICVs has an average increase of 16s on the road than HDVs, and an average of 16.8s less parking time. Therefore, the traffic flow composed of ICVs can effectively shorten the total travel time and the parking time of the vehicle in the vicinity of intersections, thereby improving the efficiency of road traffic, which provides a reference for the application and development of ICVs on the road in the future.