## 6 Experimental Setup and Results
### Cross-Source Performance  
This section presents the experimental results derived from various input data fusion models compared with our study. We evaluated downtown Milan, an area of approximately 750 grids, as illustrated in Figure 1c. The evaluation method uses CDR and other relevant datasets to measure network traffic activity. Table 5 provides a summarized overview of the performance of the various data input fusion models, considering their unique characteristics. This table illustrates the MA and RMSE values across the 750 grids. The mean accuracy metric represents the average accuracy of the model predictions, while the RMSE quantifies the overall discrepancy between predicted values and ground truth. As seen in Table 5, the performance of the INT data in isolation mirrors the results we procured in our previous paper for the Maximum (Max), Average (Avg), and Minimum (Min) tasks [13]. This implies that our architecture effectively predicts network traffic, even in suburban locales characterized by sparse network traffic, as it sustains a high accuracy under such circumstances. Assessing the data fusion models, we discern that integrating Social Pulse and Milano Today data with INT augments the performance compared to employing INT exclusively. This signifies that these supplementary data sources benefit the network traffic prediction task. Furthermore, upon contrasting the performance of various prediction methodologies, it is consistently observed that the task average MA is superior to the minimum and maximum. This suggests that our model excels at average predictions and exhibits less sensitivity towards extreme values. In summary, the accuracy attained by incorporating pertinent data sources surpasses the accuracy of the single INT forecasts by approximately 2% to 3%. This underscores the effectiveness of introducing additional data sources to enhance the accuracy of network traffic prediction. Our approach involves utilizing the temporal characteristics of the INT data by processing them simultaneously in two distinct dimensions: the preceding day and the previous week. We have noted that deep learning models are especially skilled at identifying patterns in the data when both these temporal variables are considered. By considering daily and weekly data simultaneously, we can make better predictions than if we relied only on the forecast of the fusion day. Table 6 illustrates the prediction performance of integrating daily and weekly temporal characteristics. The table shows combining data from these time scales leads to better predictions. By considering the characteristics of both temporal dimensions together, we observe an improvement in prediction accuracy, with an increase ranging from 3% to 6%, compared to using only the single INT data. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **INT** & **INT+S** & **INT+M** & **INT+S+M** \\ \hline \multirow{2}{*}{Task Max} & MA (\%) & 73.6 & 74.8 & 75.1 & **75.9** \\ \cline{2-6}  & RMSE & 206.28 & 202.24 & 200.11 & **197.54** \\ \hline \multirow{2}{*}{Task Avg} & MA (\%) & 78.9 & 79.8 & 79.7 & **80.9** \\ \cline{2-6}  & RMSE & 116.45 & 115.58 & 115.97 & **111.54** \\ \hline \multirow{2}{*}{Task Min} & MA (\%) & 72.8 & 77.8 & 78.1 & **79.3** \\ \cline{2-6}  & RMSE & 133.56 & 106.57 & 104.54 & **100.45** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Compare input data for different characteristics. This insight underscores the importance of acknowledging short-term (daily) and long-term (weekly) patterns in network traffic prediction. By simultaneously incorporating daily and weekly temporal characteristics, our model leads to significantly more accurate predictions and a holistic understanding of temporal dynamics. We have incorporated weather data into our previous analysis to measure the influence of weather conditions on data fusion and investigate their role in enhancing accuracy.