## 4 Social Media-Driven Traffic Prediction Framework with Illustrative Example
### Illustrative Example Development and Validation
#### 4.3.2 Case Studies and Validation of Results  
For Case Study A, the methodology and framework will be applied to a megacity in the United States. This megacity will provide traffic and weather information and published vehicle routes from Amazon. This application will show traffic prediction accuracy under different conditions and why drivers yield further optimized routes.  
Case Study B applies to a megacity resembling the Latin American urban infrastructure in the United States. This case study will set an example for using the framework in cities where public data is not widely available due to the city's resource limitations. Furthermore, this work will show the importance of social media in the framework since it will be the main factor for traffic prediction. Latin American countries have a grid-like urban road network, as shown in Figure 10, which can be analogous to cities such as Orlando, Miami, or New York City, given their high population of Latino communities.  
Figure 9: New GCN-LSTM System Architecture (modified from [30]).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Date** & **Wind** & **Visibility** & **Temperature** & **Dew Point** \\ \hline
2019-01-01T00:00:00 & 230 & 16,000 & 156 & 67 \\
2019-01-01T00:53:00 & 170 & 16,093 & 156 & 56 \\
2019-01-01T01:53:00 & 280 & 16,093 & 150 & 56 \\
2019-01-01T02:53:00 & 30 & 16,093 & 150 & \(-133\) \\
2019-01-01T03:53:00 & 110 & 16,093 & 139 & \(-111\) \\
2019-01-01T04:53:00 & 110 & 16,093 & 122 & \(-128\) \\
2019-01-01T05:53:00 & 110 & 16,093 & 122 & \(-122\) \\ \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Example of weather data (Los Angeles).  
Results will be validated against vehicle routing and traffic datasets in the respective countries. The proposed framework will have to match these routes with 98% accuracy or yield further optimized ones. In addition, the framework will be validated by routing data from Amazon and another retail company. Amazon released a dataset for the routing  
Figure 10: The road network from cities in Latin America.  
problem, where the city and customer locations are known. The dataset has more than 4000 driver-determined routes, representing their expertise in the delivery areas and environmental factors that come into play.  
The illustrative example for the traffic prediction methodology uses data from California's Performance Measuring System (PeMS) District 7. The data is aggregated every 5 min for the weekdays of May and June of 2012. Data preprocessing has been performed following steps from [70] and further narrowed down to a subset of 25 roads.  
Preliminary experiments show that using an extended version of this neural network, called Bidirectional LSTM (BiLSTM), can help increase the accuracy of long-term traffic predictions where trends are cyclic. For example, Table 6 shows the mean absolute error (MAE) for experiments performed using traffic and road network data from 26 roads in California's District 7 to predict traffic for the next 1 h, 6 h, 12 h, and 24 h.