## 6 Experimental Setup and Results
### Overall Performance  
In this sub-section, we will discuss the overall performance of the MSCR model in managing and analyzing diverse data sources. The MSCR model is designed to handle multi-source data fusion effectively. Table 9 summarizes various model performances--including ST-ResNet, T-DenseNet, ARIMA, and our MSCR framework--in predicting network traffic. The table depicts the MA and RMSE across the 750 grids. ST-ResNet leverages a deep residual network (ResNet) to forecast crowd traffic. It integrates periodic data and weather information to fuse the raw data. However, its reliance solely on a CNN, without including LSTM, might limit its effectiveness in capturing temporal features. Also, it does not consider other data sources like Milano Today. Despite these constraints, it achieves a superior accuracy of 73.6% in the minimum task, marking a 1% increase compared to previous work. T-DenseNet employs a dense convolutional network (DenseNet) architecture to forecast call data. This model interconnects each neural network layer, enabling efficient feature extraction and reducing the parameter count. However, T-DenseNet does not employ LSTM, thus limiting its ability to capture time-related characteristics fully. Despite this, it achieves reasonable accuracy levels, improving the prediction accuracy of the predecessor model by approximately 5% across the task maximum, task average, and task minimum. ARIMA, a conventional time-series model, achieves approximately 76.5% accuracy in the task average. However, it falls short in capturing uncertainties, such as the maximum and minimum task values, where it achieves only 64% and 68% accuracy, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & \multicolumn{3}{c}{**Early Fusion**} & \multicolumn{3}{c}{**Late Fusion**} \\ \hline \multirow{3}{*}{Task Max} & \multirow{3}{*}{IM (\%)} & **INT+S+M** & **INT+D+W** & **INT+S+M+D+W** & **INT+D+W** & **INT+S+M+D+W** &
\begin{tabular}{c} **CNN-RNN** \\ **(fusion)** \\ \end{tabular} \\ \cline{2-10}  & & **MN** (\%) & 73.6 & 72.1 & 68.9 & 71.5 & 75.9 & 79.1 & 80.6 & **81.7** \\ \cline{2-10}  & \multirow{2}{*}{RMSE} & 206.28 & 215.62 & 234.62 & 227.96 & 197.54 & 78.98 & 76.34 & **75.52** \\ \cline{2-10}  & & **MN** (\%) & 78.9 & 76.8 & 72.4 & 75.9 & 80.9 & 84.2 & 86.9 & **87.4** \\ \cline{2-10}  & \multirow{2}{*}{RMSE} & 116.45 & 118.65 & 134.51 & 127.26 & 111.54 & 65.84 & 64.01 & **59.34** \\ \cline{2-10}  & & **MN** (\%) & 72.8 & 71.8 & 67.5 & 69.9 & 79.3 & 80.5 & 83.4 & **84.1** \\ \cline{2-10}  & \multirow{2}{*}{RMSE} & 133.56 & 135.36 & 158.63 & 147.68 & 100.45 & 74.48 & 66.31 & **66.32** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Fusion comparison. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **ARIMA** & **CNN-RNN (INT)** & **ST-ResNet** & **T-DenseNet** & **INT+D+W** & **CNN-RNN (fusion)** \\ \hline \multirow{2}{*}{Task Max} & MA (\%) & 64.1 & 73.6 & 71.5 & 79.5 & 79.1 & **81.7** \\ \cline{2-7}  & RMSE & 239.35 & 206.28 & 224.36 & 78.83 & 78.98 & **75.52** \\ \hline \multirow{2}{*}{Task Avg} & MA (\%) & 76.3 & 78.9 & 78.6 & 82.9 & 84.2 & **87.4** \\ \cline{2-7}  & RMSE & 134.65 & 116.45 & 118.36 & 68.35 & 65.84 & **59.34** \\ \hline \multirow{2}{*}{Task Min} & MA (\%) & 68.3 & 72.8 & 73.6 & 80.5 & 80.5 & **84.1** \\ \cline{2-7}  & RMSE & 154.35 & 133.56 & 124.36 & 76.36 & 74.48 & 66.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of different works. This illustrates the limitations of traditional time-series models in accurately predicting network traffic. Conversely, our MSCR framework integrates LSTM to capture time dependencies effectively. By merging various data sources and utilizing LSTM, our framework secures higher accuracy rates across the task maximum, task average, and task minimum compared to ST-ResNet and T-DenseNet. Furthermore, our model surpasses the prior work by approximately 7% in prediction accuracy. In the task maximum, task average, and task minimum, our model attains accuracy levels exceeding 80%.