## 1 Introduction
This mechanism allocates the priority of information transmission to vehicles near the end of the motorcade and makes other vehicles near the stop line stop transmission. When the adjacent intersection is far away or the motorcade is a mixed traffic flow, the model cannot predict queue length. Coinciding with the basic idea proposed in [19], the number of vehicles is used to predict the queue length. An improved interpolation algorithm is designed to evaluate the estimation results at different penetration rates [22]. The results revealed that a low penetration rate would lead to large prediction error. To explore the relationship between the connected vehicle penetration rate and prediction accuracy, an algorithm about finding the minimum penetration rate is designed in [23, 24]. It concluded that the minimum penetration rate that can ensure the estimation accuracy was 1%. In the practical application, more factors impose on the estimation accuracy, especially the vehicle type, which leads to relative error. The camera-based model is capable of remedying this disadvantage. Image and vision-based traffic monitoring is widely used benefiting from the development of embedding and computer technology [25]. The vehicle recognition algorithm and typical sample data set are developed in these works. It is relatively difficult to deal with the images. Recently, lots of powerful neural networks are introduced by scholars and engineers to deal with the images due to the improvement of artificial intelligence. Deep learning using DNN has latterly shown encouraging fitness in object detection and image classification [26]. Deep learning DNN is used to monitor traffic in ITS applications [25; 27]. Deep neural network has the ability of abstracting many characters from the image. A feature extraction algorithm about traffic flow is designed to recognize the appearance-based characteristics of traffic images by DNN in [28]. Nowadays, many traffic infrastructures and vehicles can be accessed to the network and communicate useful information. Infrastructures and vehicles are connected to the road network that share information between each other. The framework has improved greatly with the development of IoT [29]. Collection and dissemination of traffic information, management and monitoring of traffic flow have been investigated widely based on the application of IoT in the transportation system [30]. Many traffic states including traffic flow, jamming, queuing length at intersections can be collected, abstracted, and fused through the connected traffic infrastructures and vehicles. These traffic states are of great significance to traffic management and optimization. In this aspect, the development of connected vehicles and wireless technology has brought revolutionary changes, which makes the traffic information exchange convenient [31]. Benefiting from the development of embedded devices, the computing capability of the connected traffic infrastructures' controller enhances greatly. Moreover, the cloud [32] and edge computing [33] can be used to serve the traffic states sensing. A framework of smart city with intelligent IoT, cloud, edge, and 5G is constructed to provide more efficiency and rationality [34]. It supports a variety of innovative applications and services. There are some application cases based on IoT to serve for traffic. A predictive maintenance service is studied for connected cars based on IoT. The status of key parts and of the car as a whole are transmitted to the cloud and a predictive maintenance plan is given based on the current status, which will decrease the cost of maintenance and eliminate safety hazards if compared to a traditional periodical maintenance schedule [35]. Another useful application is parking service for connected cars based on IoT. A comprehensive review of smart parking research focused on distinguishing their functionalities and controversial issues is provided in [36].