# Traffic-Aware Optimization of Task Offloading and Content
## 4 Joint Optimization for Offloading and Content Caching with Traffic Stream Prediction
### FOST Enabled Traffic Stream Prediction
#### 4.1.2 Spatial-Temporal Correlation Extraction  
Traffic flows often showcase intricate temporal and spatial patterns. FOST excels in dissecting these nuances, delving into both long-term and short-term trends within  
Figure 2: Queen adjacency.  
traffic data by adeptly extracting temporal correlations. Additionally, leveraging a spatial correlation analysis enables FOST to unravel the intricate relationships between traffic flows in distinct locations. Consequently, FOST's proficiency in discerning temporal and spatial correlations not only refines predictions with a fine-grained precision, surpassing global estimates, but also empowers the model to more effectively apprehend changes and patterns inherent in the data.  
In the realm of modeling, FOST's module leverages the preprocessed dataset to employ advanced architectures such as multilayer perceptron (MLP) and recurrent neural networks (RNNs). These architectures adeptly extract temporal correlations embedded in the training dataset. Furthermore, the integration of graph neural networks (GNNs) facilitates the capture of spatial correlations among nodes in graph datasets. The fusion module is intricately designed to autonomously select and amalgamate model predictions. This innovative design empowers FOST with the flexibility and intelligence to dynamically adjust model combinations, ensuring heightened precision and robustness in predictions when confronted with complex forecasting tasks. In the specific domain of vehicle traffic prediction, FOST recognizes the imperative consideration of not only temporal fluctuations but also spatial interactions. Acknowledging that traffic dynamics within a particular area are intricately linked to neighboring regions, FOST underscores the significance of spatial correlations that cannot be overlooked.  
Hence, the extraction of both temporal and spatial correlations from traffic stream data becomes essential. To achieve this, the time series data encompassing recent temporal intervals, including cumulative traffic flows entering and exiting the region during each time slot, are input into an underlying deep temporal neural network module. Subsequently, the temporal module of the model undertakes the task of assimilating patterns from historical data, subsequently encoding them as a collection of vectors within a latent space.  
Furthermore, a crucial step involves the integration of spatial information through a mechanism of superimposing information onto the temporal patterns of adjacent spatial units. This facilitates a comprehensive aggregation of spatial insights and patterns, enhancing the predictive capabilities of the model.  
We performed an in-depth comparison between the BikeNYC real dataset and FOST's traffic flow predictions at a specific time instance. The comparative findings are depicted in Figure 3. Evidently, post-adequate training, FOST demonstrates exceptional accuracy in predicting the traffic flow within a 16 \(\times\) 8 region. This remarkable performance lays a robust and dependable groundwork for subsequent endeavors in optimizing task offloading and edge caching, leveraging the insights from traffic flow prediction.