# Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication
## 3 Experimental Results and Discussion
### Comparison Experiment Results  
To verify whether the proposed model can effectively extract the spatiotemporal features from the dataset of traffic state, we compare the GCN-GRU model with GRU and GCN model in the experiment. As shown in Figures 13 and 14, the average traffic flow and the mean speed capacity at the time points \(5\), \(10\), \(15\), and \(30\)-min are compared, respectively, and then the error performances are analyzed intensively. In the beginning \(5\) min, by comparing with the GCN model which only considers spatial features, the predictions for the average traffic flow, the \(RMSE\) and \(MAE\) show the superiority of our proposed GCN-GRU model with \(33.35\%\) and \(34.4\%\) reductions. In the same way, for the \(10\), \(15\), and \(30\)-min prediction of the average traffic flow, the \(RMSE\) performed in our GCN-GRU model is reduced by \(30.89\%\), \(37.51\%\) and \(39.53\%\), respectively, and the \(MAE\) is reduced by \(33.66\%\), \(36.9\%\) and \(34.95\%\), respectively. Thus, it can be clearly observed that \(RMSE\) and \(MAE\) errors of the proposed model have been significantly reduced. We infer that our proposed GCN-GRU model can extract spatial features efficiently. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Time**} & **Evaluation** & \multirow{2}{*}{**GCN-GRU**} & \multirow{2}{*}{**LSTM**} & \multirow{2}{*}{**RNN**} & \multirow{2}{*}{**CNN**} & \multirow{2}{*}{**BP**} \\  & **Index** & & & & & \\ \hline \multirow{3}{*}{\(5\) min} & _MAE_ & 0.2911 & 0.5451 & 0.9528 & 1.4213 & 0.9984 \\  & _RMSE_ & 0.2935 & 0.6654 & 1.0981 & 1.7520 & 1.2135 \\  & _Accuracy_ & 0.965 & 0.852 & 0.781 & 0.625 & 0.773 \\ \hline \multirow{3}{*}{\(10\) min} & _MAE_ & 0.2133 & 0.5431 & 0.9413 & 1.5312 & 0.9988 \\  & _RMSE_ & 0.2465 & 0.6641 & 1.0005 & 1.7640 & 1.2141 \\  & _Accuracy_ & 0.967 & 0.864 & 0.788 & 0.631 & 0.771 \\ \hline \multirow{3}{*}{\(15\) min} & _MAE_ & 0.1835 & 0.5394 & 0.9641 & 1.4812 & 0.9985 \\  & _RMSE_ & 0.2004 & 0.6604 & 1.2154 & 1.7024 & 1.2031 \\  & _Accuracy_ & 0.966 & 0.865 & 0.791 & 0.640 & 0.773 \\ \hline \multirow{3}{*}{\(30\) min} & _MAE_ & 0.1585 & 0.5388 & 0.9721 & 1.5531 & 0.9971 \\  & _RMSE_ & 0.1970 & 0.6541 & 1.3412 & 1.7233 & 1.1998 \\ \cline{1-1}  & _Accuracy_ & 0.965 & 0.859 & 0.799 & 0.627 & 0.772 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison results of the mean speed capacity. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Time**} & **LSTM** & **RNN** & **CNN** & **BP** & **GCN–GRU** \\ \hline Training time & 183.2 s & 105.4 s & 309.7 s & 123.4 s & 112.3 s \\ Prediction time & 1.353 s & 0.932 s & 2.332 s & 1.234 s & 0.995 s \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The computational cost of LSTM, RNN, CNN, BP, and GCN–GRU. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Time**} & **Evaluation** & \multirow{2}{*}{**GCN–GRU**} & \multirow{2}{*}{**LSTM**} & \multirow{2}{*}{**RNN**} & \multirow{2}{*}{**CNN**} & \multirow{2}{*}{**BP**} \\  & **Index** & & & & & \\ \hline \multirow{3}{*}{\(5\) min} & _MAE_ & 1.0002 & 1.9823 & 3.5521 & 4.3511 & 3.8552 \\  & _RMSE_ & 1.2354 & 2.9940 & 5.3215 & 6.5362 & 4.5113 \\  & _Accuracy_ & 0.949 & 0.832 & 0.775 & 0.682 & 0.751 \\ \hline \multirow{3}{*}{\(10\) min} & _MAE_ & 0.9303 & 1.9823 & 3.3241 & 4.3012 & 3.8463 \\  & _RMSE_ & 1.1532 & 2.9940 & 4.9215 & 6.4621 & 4.4963 \\  & _Accuracy_ & 0.954 & 0.832 & 0.779 & 0.682 & 0.758 \\ \hline \multirow{3}{*}{\(15\) min} & _MAE_ & 0.8049 & 1.9838 & 3.4843 & 4.2984 & 3.8466 \\  & _RMSE_ & 0.9803 & 2.9843 & 5.0122 & 6.4551 & 4.4985 \\  & _Accuracy_ & 0.958 & 0.845 & 0.780 & 0.671 & 0.756 \\ \hline \multirow{3}{*}{\(30\) min} & _MAE_ & 0.7842 & 1.8856 & 3.5012 & 4.3123 & 3.8512 \\  & _RMSE_ & 0.9053 & 2.7650 & 5.1230 & 6.5010 & 4.5013 \\ \cline{1-1}  & _Accuracy_ & 0.967 & 0.883 & 0.789 & 0.663 & 0.758 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison results of the average traffic flow.