## 5 Proposed Models
### Architecture Overview
#### 5.3.1 Processing Grid Data  
The structure illustrated in Figure 3 accepts six data types as input. Five of these are directed into the neural network, preserving a consistent architecture. One of the input data types is internet traffic (INT), which is part of the call detail records (CDRs) and is represented by \(X_{n}\): \(\{X_{0},\ldots,X_{t-1}\}\). Each data point in INT corresponds to a \(100\times 100\) grid map, with each interval spanning 10 min. However, our study only considers a \(15\times 15\) section of the \(100\times 100\) grid map as the INT input. Moreover, since we focus on forecasting for Milan's city center, we treat the central \(3\times 3\) area as the target label. Furthermore, we analyze the correlation coefficient between the internet traffic and the two data sources, aiming to quantify:  
* The magnitude and direction of the impact of the two datasets on each other. * The correlation direction indicates whether they co-occur or have an inverse relationship. We extract periodic features from INT by selecting different timestamps, as INT exhibits both temporal and periodic characteristics. As depicted in Figure 4, the network traffic consistently peaks simultaneously every day. This repetitive pattern reflects the daily routine of people using the network, with peak usage typically occurring between 11:00-13:00 and 17:00-19:00, represented in hours. Additionally, the weekly pattern affects network traffic, varying significantly between weekends and weekdays, with generally lower and more variable traffic on weekends. We extract features over daily and weekly time spans to capture these patterns, denoted by \(l_{d}\) and \(l_{w}\), respectively. Specifically, we collect data points from one-day and one-week periods before the prediction time \(t\), where the number of data points \(d\) and \(w\) is 24 and 168, corresponding to 10-minute intervals in one day and one week, respectively. Thus, the periods for \(INT_{\text{day}}\) and \(INT_{\text{week}}\) are marked as \(t-25\) and \(t-169\) in Figure 3, respectively. These extracted features are labeled as \(D_{n}\): \(\{D_{0},\ldots,D_{t-(l_{d}-1)\times d}\}\) and \(W_{n}\): \(\{W_{0},\ldots,W_{t-(l_{w}-1)\times w}\}\). The INT referred to earlier is the original data, i.e., the regular data before feature extraction. In summary, the periodic features extracted from INT capture the daily and weekly traffic patterns, subsequently utilized as inputs to the neural network for predicting traffic flow in the Milan city center. The Milano Today and Social Pulse datasets include news articles, tweet records, and the corresponding coordinates where the events occurred. The data trends for Milano Today (MT) and Social Pulse (SP) are illustrated in Figure 5a,b, showing similarities to the trends observed in INT. These figures indicate that the length of news articles and the volume of tweets increase concurrently with network traffic, suggesting a linkage between these datasets and the daily and weekly patterns of INT traffic. Figure 4: Internet traffic over an hour. According to Figure 6, most grid correlations exceed 0.7, with a correlation coefficient of 0.756 between internet traffic and Social Pulse and 0.831 between internet traffic and Milano Today. A correlation coefficient above 0.7 denotes a strong correlation, with darker colors indicating more vital relevance. Therefore, we infer that these two data sources are highly relevant to internet traffic, justifying their use as inputs in our model to enhance network traffic predictions. For Social Pulse and Milano Today, the time of occurrence is accurately recorded. However, we align these data with the internet traffic intervals, counting the number of events every 10 min and converting the coordinates of each data point into a \(7\times 7\) matrix. This adjustment accounts for the data's spatial resolution, given that the error range of Milano Today and Social Pulse is approximately 600 m, and each grid in our dataset represents an area of approximately 200 \(\times\) 200 meters.