# Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication
## 1 Introduction
However, CNN model, regarding spatial relations as simple Euclidean structures, is not suitable for dealing with traffic networks with spatiotemporal characteristics. To solve the above shortcomings, the graph convolution neural network (GCN) displayed in CNN is extended in [19]. [PERSON] et al. [20] realized GCN through an adjacency matrix based on the traffic network topology and identified the most influential road sections in the traffic network. However, the adjacency matrix was heuristic, which reflected that the spatial relationship between the vertexes of the road network is not accurate. In order to make up for the shortcomings of the graph convolution model based on heuristic adjacency matrix, [PERSON] et al. [21] proposed a self-learning adjacency matrix and designed different adjacency matrices on different convolution layers. However, the use of multiple convolution layers in the traffic network will lead to the lack of explanation of the spatial relationships. [PERSON] et al. [22] proposed a traffic learning model based on GCN. In this model, the traffic flow was learned as an image, and the spatiotemporal traffic dynamics were transformed into a two-dimensional spatiotemporal matrix, which can describe the spatiotemporal correlations of traffic flow. [PERSON] et al. [23] proposed three-channel networks, including GCN, GRU, and fully connected neural network (FCNN) to extract spatial, temporal, and other features, respectively. Based on the above investigations, the challenges of traffic state prediction can be summarized as follows:  
* How to perceive and predict traffic state in real time. The traditional traffic sensors are difficult to meet the predicted requirements of short-term traffic state prediction, especially for the accuracy requests of intelligent connected vehicles (ICVs). * How to perceive and analyze the dynamic data of vehicles. The vehicles running on the road may face all kinds of sudden traffic incidents. However, traditional vehicular dynamic data strategies (such as floating vehicle) are difficult to meet the requests of accurate prediction in real time. * How to effectively analyze and filter spatiotemporal features of traffic state. The spatiotemporal features of the traffic state are high nonlinear correlations, which are still the focus problems of urban traffic research. For example, the variation of traffic flow at one intersection will affect the traffic state at the adjacent intersection, meanwhile, affect the future traffic state over time. To solve the above problems, this paper proposes a traffic perceptual and computational system, which incorporates the roadside intelligent sensors, ICVs, and traffic signal controller at the intersection. It can accurately perceive not only each single vehicular state, but also the whole intersection traffic state. The main contributions of the paper can be summarized as follows:  
* Firstly, this paper proposes a traffic perceptual and computational system based on the MEC architecture, in which each edge server is responsible for managing the data upload of vehicles within its service scope. Moreover, the MEC server will predict traffic state based on the perceptual information. * Secondly, this paper applies on board unit (OBU) data of ICVs to predict traffic state by tracking vehicular driving state in real time, which can effectively improve the accuracy of prediction. * Thirdly, with the characteristics of the MEC, this paper designs a traffic prediction model to analyze and evaluate the traffic state at the intersection in V2X environment. GCN and GRU models are combined to analyze spatiotemporal features of traffic data in the model. Then, the soft-attention mechanism is utilized to integrate the various extracted features. The reminder of this paper is organized as follows. The traffic state perception system of an intersection scenario, which is based on MEC architecture in V2X communication, is described in Section 2, where a predicted model of short-term traffic state is then constructed and clarified.