# Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication
## 1 Introduction  
Due to considerable urbanization in recent years, the increasing number of vehicles in cities has led to various traffic problems such as traffic congestion, accidents and environmental degradation. According to the state-of-the-art, the main solutions to these problems lie in improving the traffic capacity of the road [1, 2, 3]. However, most studies concentrate on in-car advisory systems on lane, speed, and headway [4], strategies design on traffic networks [5], and different model managements [6]. Traditional single-component sensors are difficult to meet the predicted requirements of complex traffic networks. Therefore, more efficient and effective approaches still need to be explored to improve the traffic capacity. Along with the development of communication technologies, the mobile communication networks are expected to be established in people, vehicles, and roads for transforming and transferring real-time information accurately. Vehicle-to-everything (V2X), fusing on various vehicles and roads information, has been treated as the core technology for the next generation of intelligent transportation system (ITS) to perceive real-time traffic state on the roads [7], which faces huge real-time data processing problems in transmission quality. In order to solve the problems, in this paper, an ITS is proposed by integrating vehicles and roads with wireless communication technologies for flexible informationsharing and accurate traffic state prediction. The delay of data transmission can be greatly reduced and the efficiency of data processing can then be effectively improved with the constructed mobile edge computing (MEC) platform in V2X network [8]. The development of these technologies plays a key role in the short-term traffic state prediction, which involves the information of vehicle driving state, road environment, and other traffic state messages. Through the support of these technologies, the efficient traffic state management can be guaranteed. The short-term traffic state prediction has been conducted in many studies, which refers to the next stage of traffic state prediction with no more than 30 min [9]. The employed approaches can be divided into two types: model-driven oriented and data-driven oriented. The model-driven methods describe the traffic state by building a macroscopic model, which mainly includes various Kalman filtering algorithms [10], and the data-driven method extracts spatiotemporal features from historical traffic data and applies deep learning to predict the traffic state [11]. Based on our intensive investigations, the data-driven method, as the main means for traffic state prediction, is utilized in most of the research. [PERSON] et al. [12] proposed a traffic flow prediction method based on deep learning by considering the spatiotemporal correlation of traffic flow, where an automatic encoder model was used to learn general traffic flow characteristics and train them in a hierarchical manner. This work was the first opportunity to use an automatic encoder as a building block to represent a deep architectural model for predicting traffic flow characteristics. [PERSON] et al. [13] established a depth neural network based on long short-term memory (LSTM) to predict traffic flow during peak hours. This method identified the unique characteristics of traffic data, and further improved the prediction model of mixed depth LSTM model. However, these models rarely take the spatial feature of the traffic network into account. Moreover, convolution neural network (CNN) and recurrent neural network (RNN) are widely applied to a variety of machine learning tasks because of their strong hierarchical feature learning ability [14]. They are widely used in the fields of computer vision [15], natural language processing [16], and time series data prediction [17]. RNN and its variant model, such as the gated recurrent unit (GRU), can effectively extract temporal dependencies [18].