# Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication
## 2 Materials and Methods
### Traffic State Perception
#### 2.1.2 Traffic Perception Data  
The purpose of our system is to construct a stereoscopic and accurate perception database of the intersection. By comparing our system with the traditional traffic perception methods, the performance of the constructed system will be observed that whether it can realize more complex information interaction between vehicles and roads, and perceive the driving status of a single vehicle and roadside traffic environment information.  
1. Data from Roadside Sensors  
In the system, roadside sensors include LiDAR sensors and cameras, by which real-time states of each target within the range of the sensors at the intersection can be easily obtained. To prevent the impact of pedestrians and other traffic participants, vehicles at the intersection are selected as targets to predict the traffic state in this system.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Data Sources** & **Data Types** \\ \hline \multirow{4}{*}{Roadside sensors} & Vehicle information: license plate number, latitude, longitude, \\  & speed, horizontal distance, heading angle, etc. \\ \cline{2-3}  & Traffic state information: average vehicular speed, average \\  & traffic flow, average queue length, parking line location, etc. \\ \hline \multirow{2}{*}{OBU} & Timstamp, latitude, longitude, speed, acceleration, license \\  & plate number, wheel speed, steering angle, braking status, etc. \\ \hline \multirow{2}{*}{Traffic signal controller} & Signal cycle, signal phase, traffic light color, remaining time of \\  & green, etc. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Types and sources of traffic perception data.  
Figure 2: Traffic perception system based on V2X communication.  
Based on the data fusion of the point cloud and the image, the accurate information of each vehicle, including the license plate number, the latitude, the longitude, the speed, the horizontal distance, the heading angle, and so on, can be perceived. Moreover, LiDAR sensors and cameras can also perceive the traffic state of the intersection, including the average vehicle speed, the average traffic flow, the average queue length and the parking line location.  
2. Data from OBU  
OBU data consist of the real-time information of ICVs while driving at the intersection. Therefore, millions of observations and terabytes of data are generated every day [25]. In this system, the recorded information from OBU contains an anonymous identifier (ID), the timestamp, the GPS position (latitude, longitude), the speed, the acceleration, the license plate number, the wheel speed, the steering angle, and so on. Additionally, OBU data includes vehicular braking states.  
3. Data from Traffic Signal Controller  
Traffic signal control is a fundamental element in traffic guidance at urban signalized intersections [26; 27]. The core of the integration between traffic signal control and traffic guidance is in temporal and spatial synchronization [28]. At the macro level, traffic signal control can actively guide the drivers to choose the path by combining the traffic guidance information to balance the traffic pressure. At the middle and micro level, the spatial variables, such as the number of lanes, lane functions, and traffic flow directions, are combined with the temporal variables to achieve more efficient traffic optimization, including green signal ratio, phase sequence and phase difference of signal light. In our system, the signal cycle, the signal phase, the traffic light color, the remaining time of green, etc., are involved in the traffic signal controller.