# Citywide Cellular Traffic Prediction Based on a Hybrid Spatiotemporal Network
## 4 Experimental Results and Analysis
### Experimental Result  
In this part, we use HSTNet to predict the dataset and analyze the results. To verify the predictive performance of our model, we compared the predicted and actual values of the city's total traffic over one week and performed error analysis.  
As shown in Figure 11, the X-axis represents the time interval in hours, and the Y-axis represents the total traffic for the entire city at the current time. Even for the difficult task of predicting overall urban traffic, the model still had a good fitting effect. A sharp rise in traffic was observed at 145 h in the figure due to the large error caused by the 2013 New Year's Eve event. The ability of the model to fit the traffic fluctuations caused by unexpected events needs to be improved.  
HSTNet had the best prediction effect on the Call dataset. From the top right subgraph of Figure 11, it can be seen that the error value could be controlled within a certain range except for the impact caused by the unexpected event. The prediction effect of Internet traffic at night was not very  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Input** & **1** & **2** & **3** & **4** \\
**Dimension** & & & & \\ \hline SMS & 27.51 & 27.18 & **26.42** & 26.83 \\ Call & 16.86 & 16.23 & **16.04** & 16.62 \\ Internet & 80.10 & 75.38 & **72.72** & 78.32 \\ \hline \hline \end{tabular}
\end{table}
Table 3: RMSE results of different input dimensions.  
Figure 10: Comparison of the baseline and HSTNet on cell (50,58).  
satisfactory. There was a significant error in traffic prediction during this period, as shown in the lower right subgraph, because the three types of traffic had different scales of change. The Internet's day-night traffic gap was approximately two million (SMS and Call were only approximately 500 k), which created considerable difficulties in forecasting.  
HSTNet also had good predictive performance for traffic in different areas of the city. Figure 12 shows a comparison of the predicted and real images at 10 o'clock on 24 December 2013. Each image had 100 \(\times\) 100 cells. The brightness of the cell pixels indicates the traffic load of the corresponding cell. The brighter area indicates greater traffic load. This proved that our model could accurately predict the regions with different traffic distributions and could extract the spatial correlation of urban cellular traffic.  
The last line in Figure 12 shows the error between the corresponding prediction map and the ground truth. The brighter area indicates a greater error. We can see that the prediction error was not only biased towards the downtown area where the traffic load was large, but also, there were large errors in many suburbs with heavy traffic loads. This shows that the change in cellular traffic also depended on many factors that we had not considered, and a more sophisticated model is needed in the future to analyze the characteristics of cellular traffic changes.  
Figure 11: Comparison of hourly traffic prediction with the ground truth.  
Figure 12: Comparison of predicted and real images.