# Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication
## 3 Experimental Results and Discussion
### Performance of Prediction Model  
Based on the perception dataset of the traffic state, the experiment predicts the average traffic flow and the mean speed capacity of urban intersections in a short term. The specific analyses are presented as follows.  
Figure 10(a) shows the performance of the training model with dividing the dataset of the average traffic flow by 7:3 for training and testing. Figure 10(b) shows the fitting results of the actual average traffic flow data and the predicted results, as well as the errors between the predicted values and the real observed values. It is worth noting that the average traffic flow of the intersection has spatiotemporal periodicity, and the model accurately predicts the trend at the next time step by learning the spatiotemporal correlation of data. The values of \(RMSE\) and \(MAE\) are 0.9823 and 0.8049, respectively, which prove that the prediction results can meet the requirements [32; 33].  
Figure 11: _Cont._  
Figure 10: The comparison of prediction _Accuracy_ under different hidden neurons.  
Figure 11(a) shows the performance of the training model with dividing the dataset of the mean speed capacity by 7:3 for training and testing. For the threshold is set up as 0.6, the time of the mean speed capacity exceeding the threshold can be observed obviously. The prediction results can be treated as a reference to evaluate the short-term traffic state of the intersection in real time.  
Figure 11: Prediction results of the average traffic flow. (**a**) The training performance of real value and predicted value in average traffic flow; (**b**) Fitting results of the actual average traffic flow data and the predicted results; errors between the predicted values and the real observed values.  
As shown in Figure 12b, the fitting results between the real observation valued and the predicted valued of the mean speed capacity meets the predicted requirements [34]. The values of \(RMSE\) and \(MAE\) are 0.1970 and 0.1585, respectively, which can prove that the prediction model is effective.  
Through the prediction of the average traffic flow and mean speed capacity, the traffic operation state of intersections can be perceived in real time. At the same time, it can provide data support for traffic managers to conduct real-time traffic guidance at intersections and relieve the queuing pressure at intersections.  
In order to validate effectiveness of the proposed model, we compare it with the other four predicted models, including LSTM, RNN, CNN, and back propagation (BP) neural network models. In the process of prediction, we choose the same parameters described in Section 3.3. Tables 5 and 6 show comparison results of the average traffic flow and mean speed capacity for 5, 10, 15, and 30 min, respectively.  
In addition, the experiments also compare the training time and prediction time between the GCN-GRU model and the other four models, as shown in Table 7.  
It can be seen from Table 7 that the training time and prediction time of GCN-GRU model are longer than that with the RNN model. However, the prediction accuracy of GCN-GRU model is much higher than that in other models. We can infer that the proposed method is practical and significant in the objective of improving the predicted accuracy. To sum up, the prediction performances of GCN-GRU are better than that with other neural network prediction models in prediction accuracy.  
Figure 12: Prediction results of the mean speed capacity. (**a**) The training performance of real value and predicted value in mean speed capacity; (**b**) Fitting results between the real value and the predicted value of the mean speed capacity; errors between the predicted values and the real observed values.