Effective traffic prediction is crucial due to a surge in deliveries by commerce and urbanization. This has led to a notable rise in traffic within megacities, causing route delays to the final destinations and countless vehicle accidents. E-commerce has been in a constant boom, as buying something online and having it delivered to the front door is easier than going to the store. As more people engage in this activity, e-commerce platforms' challenges are more complicated and need to be addressed faster. However, these challenges escape the delivery company's scope when external factors influence the objective of optimized deliveries, for example, traffic issues or bad weather during the last mile, issues that are only exacerbated where traffic sensors are not widely used (i.e., underdeveloped countries). The main contributions of this research are to (1) provide a contextual foundation of current frameworks used for traffic prediction, (2) use social media and multi-modal traffic-related data (weather, points of interest, calendar of events) by leveraging social network analysis to improve the accuracy of traffic prediction, and (3) to show a methodology that can be used for partially observed traffic. The proposed methodology includes deep learning tools like Long-Short Term Memory Networks, attention mechanisms, Graph Convolutional Networks, and social media tools like sentiment analysis.